Input: Task: pass the claim using the given text. Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: (Noroozi et al. , unpublished data). A typical representative of the local endemic flora is Acantholimon (Plumbaginaceae; Fig. 4b), which is highly diverse in this area50,51. Additionally, the Yazd-Kerman harbours isolated occurrences of species otherwise distributed in Hindukush and mountains, especially in alpine regions30,49,52,53. Considerable differences can be observed among areas of endemism with respect to the richness of the 10 largest genera of the Iranian endemic vascular flora (Fig. 4b), as shall be illustrated with the following examples. Astragalus (Fabaceae), Allium (Alliaceae) and Centaurea (Asteraceae) are well represented in the Azerbaijan Plateau, but Acantholimon (Plumbaginaceae), Cousinia (Asteraceae) and Echinops (Asteraceae) are underrepresented in this area. Acantholimon, Echinops and Nepeta (Lamiaceae) are very well represented in Yazd-Kerman and Cousinia is very diverse in Kopet Dagh-Khorassan. Oxytropis (Fabaceae) is diverse in both Alborz and Kopet Dagh-Khorassan, but very poor in Zagros. The proportion of Silene (Caryophyllaceae) is high in Zagros, and Dionysia (Primulaceae) is a Zagros element with 29 endemic species in this mountain range, three species in Yazd-Kerman and only one species in Alborz. Elevational distribution of endemicsThe elevational distributions of surface area, non-endemic and endemic vascular plant species of Iran are displaced and peak at different elevations, as has already been found before on a taxonomically much smaller data set19. Despite a high proportion of surface area in lowlands (−26 to 1400 m), the proportions of non-endemics and especially of endemics in this elevation zone are low (Fig. 7). At mid elevations (1400 to 2800 m a. s. l. ), the proportion of both non-endemics and endemics are high. At high elevations (2800 to 5671 m a. s. l. [SEP] Justify:

Output: The text passes in relevance to the claim as it focuses on specific biodiversity areas in Iran and discusses the endemism aspects there, rather than addressing the Amazon rainforest or directly corroborating or contrasting the definition of biodiversity hotspots as presented in the claim.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The first term, 𝐗ϕ-𝐀OLS𝐗, consists of the regression residuals that cannot be accounted for by any linear transformation of 𝐗. It is straightforward to verify that these regression residuals, 𝐗ϕ-𝐀OLS𝐗, are orthogonal to 𝐗 (Hastie et al. , 2009, Section 3. 2) and hence also orthogonal to (𝐀OLS-𝐀q)𝐗. This orthogonality allows us to split the loss function into two terms, ∥Xϕ−AqX∥2=∥Xϕ−AOLSX∥2+∥AOLSX−AqX∥2, where the first term captures the (unavoidable) error of the least squares fit while the second term describes the additional loss suffered through the rank constraint. Since the first term does not depend on 𝐀q, the problem reduces to minimizing the second term. To minimize the second term, we note that the best rank-q approximation to 𝐀OLS𝐗 is given by its first q principal components (Eckart-Young-Mirsky theorem). Accordingly, if we write 𝐔q for the matrix of the q leading principal directions (left singular vectors) 𝐮i of 𝐀OLS𝐗, then the best approximation is given by 𝐔q𝐔q⊤𝐀OLS𝐗 and hence 𝐀q=𝐔q𝐔q⊤𝐀OLS. To summarize, the reduced-rank regression problem posed above can be solved in a three-step procedure: Compute the OLS solution 𝐀OLS=𝐗ϕ𝐗⊤(𝐗𝐗⊤)-1. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses regression residuals, orthogonality, and least squares fitting within a statistical context. There are no references to memory recall or stimuli that would relate to the assertion about memory.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: These fungi included Amphinema, which was strongly associated with +SB enzyme patterns, and Piloderma, Cortinarius and Tylospora, which were strongly associated with FH enzyme patterns. Figure 4Relationship between fungal communities and the potential enzyme activities in hyphal trap bags and the FH layers of the forest floor. −SB, hyphal trap bags with no sand barrier; FH, in situ fermentation-humic layer; +SB, hyphal trap bags with sand barrier. Joint plots vectors indicate the strength and direction of significant (P<0. 05) correlations between enzyme PC-loading factors and fungal communities at the level of (a) ecological function, (b) phyla and (c) genus or closest identity. The direction and strength of enzyme correlations contributing to each PC axis (as in Figure 3) are indicated with the abbreviations Cel, cellobiohydrolase; Glu, β-1,4-glucosidase; N-Ag, β-1,4-N-acetylglucosaminidase; Pho, acid phosphatase; Sul, sulfatase; peptidase, leucine aminopeptidase; Lac, laccase; Perox, peroxidase. Open in new tabDownload slide large fraction of terrestrial ecosystem C pools, with estimates as high as 1700 PgC (and Boisvenue, 2012), are sequestered as SOM in the soils of temperate and boreal forests (Schmidt et al. , 2011). Increased temperature and atmospheric CO2 levels associated with climate change are predicted to increase the decomposition of this SOM-C pool (Fierer et al. , 2005; Conant et al. , 2008; Craine et al. , 2010). The majority of this SOM is found in the FH horizons dominated by EMF and not saprotrophic fungi (Lindahl et al. , 2007). Quantifying the contribution of EMF mycelia to SOM turnover is, therefore, of critical importance to Cmodels (Cairney, 2012). Potential contribution of EMF mycelia to soil organic C turnover It has generally been believed that saprotrophic fungi bear primary responsibility for recalcitrant SOM-C decomposition in forest soils (Baldrian, 2008). Recently, Talbot et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the roles of various fungi, particularly mycorrhizal fungi (EMF), in the turnover of soil organic matter (SOM). It highlights the importance of quantifying the contribution of EMF mycelia to SOM turnover, indicating that certain fungi are indeed involved in the decomposition process which contributes to nutrient cycling. This aligns well with the claim regarding the role of fungi in decomposing organic matter.


Input: Task: pass the claim using the given text. Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: 2984 (2017). 10. 1016/j. cub. 2016. 09. 045 (2016). 10. 2307/2413466 (1994). 10. 1007/s00035-015-0160-4 (2016). 10. 1038/s41598-018-28504-9 (2018). 10. 1111/jbi. 12185 (2014). 10. 1111/j. 1095-8339. 2004. 00310. x (2004). 10. 1111/j. 1095-8339. 2007. 00707. x (2007). 10. 1017/S0269727000008976 (1986). 10. 11646/phytotaxa. 249. 1. 5 (2016). 10. 11646/phytotaxa. 249. 1. 4 (2016). 10. 1007/s10531-007-9246-7 (2008). 10. 1007/s10531-011-0029-9 (2011). 10. 1016/j. flora. 2006. 10. 004 (2007). 10. 3372/wi. 38. 38201 (2008). 10. 1007/s10531-016-1254-z (2017). 10. 1080/17550874. 2012. 698660 (2011). 10. 1111/avsc. 12031 (2014). 10. 1007/BF00936283 (1987). 10. 1002/tax. 581016 (2009). 10. 1093/botlinnean/box033 (2017). 10. 11646/phytotaxa. 307. 2. 4 (2017). 10. 1017/S0269727000009003 (1986). 10. 1127/0340-269X/2010/0040-0478 (2010). 10. 1127/phyto/2017/0108 (2017). 10. 1111/1365-2745. 12463 (2015). 10. 1111/geb. 12469 (2016). 10. 1046/j. 0305-0270. 2003. 00994. x (2004). 10. 1073/pnas. 0601928103 (2006). 10. 1146/annurev-ecolsys-102710-145113 (2012). 10. 5880/SFB806. 15 (2015). Zohary, M. [SEP] Justify:

Output: The text consists of a series of DOI citations and does not provide any information or context regarding biodiversity hotspots or their characteristics. Therefore, it is irrelevant to the claim about biodiversity hotspots and does not support or contrast it.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Perform PCA of 𝐀OLS𝐗 and take the q leading principal components to obtain the best low-rank approximation: 𝐀q=𝐔q𝐔q⊤𝐀OLS where 𝐔q is the N×q matrix of the q leading principal directions (left singular vectors) of 𝐀OLS𝐗. Factorize the matrix 𝐀q into decoder and encoder matrices, 𝐀q=𝐅𝐃, by choosing 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀OLS. Conveniently, the extracted decoder/encoder pairs do not depend on how many pairs are extracted: the i-th pair is given by 𝐟=𝐮i and 𝐝=𝐮i⊤𝐀OLS, independent of q. Indeed, this feature motivated the above choice that 𝐅 should have orthonormal columns. a detailed protocol A standard way to avoid overfitting in regression problems is to add a quadratic penalty to the cost function, which is often called ridge regression (RR). This approach can be used in reduced-rank regression as well. Specifically, we can add a ridge penalty term to the loss function Lϕ: Lϕ=∥Xϕ−FDX∥2+μ∥FD∥2. The RR solution modifies the OLS solution from above to ARR=XϕX⊤(XX⊤+μI)−1. In turn, the reduced-rank solution can be obtained as described above: 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀RR where 𝐔q are the first q principal directions of 𝐀RR𝐗. We found it convenient to define μ=(λ∥𝐗∥)2, since this makes the values of λ comparable across datasets. As explained below, we used cross-validation to select the optimal value of λ in each dataset. [SEP] Justify:

Output: The text does not address the claim regarding memory recall. Instead, it discusses mathematical methods including PCA, low-rank approximations, and ridge regression, which are unrelated to the topic of memory or its recall. Therefore, the text is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: (2013) concluded that EMF and saprotrophic communities have complementary roles in SOM turnover. Specifically, in pine forests in a Mediterranean climate, they found that EMF species richness explained SOM-nitrogen (N) decomposition patterns, whereas saprotrophic community structure explained SOM-C decomposition patterns. In the current study, we directly measured the potential enzyme activity in FH-filled mesh bags colonized by the two different trophic groups (colonization by obligate saprotrophs controlled by the presence or absence of a sand barrier) in a sub-boreal spruce system, and compared those activities with that of the surrounding FH horizon. We found that communities dominated by EMF hyphae exhibited significant potential to degrade both SOM-C and SOM-N. The oxidative and hydrolytic enzyme activities exhibited by these EMF communities were comparable to or higher than those measured in communities enriched with saprotrophic fungi. None of the measured enzymes were specifically associated with saprotrophs, suggesting that it is premature to generalize about the role of these two trophic groups across ecosystems. The EMF-dominated mesh bag communities and FH horizon had significantly similar patterns of cellulolytic and hemicellulolytic enzyme activity. Although these C-targeting hydrolase activities are generally attributed to saprotrophic fungi (Talbot et al. , 2013), this was not the case in our spruce soils where communities with abundant saprotrophs exhibited lower hydrolytic enzyme activity. The single-most abundant taxonomic group that correlated with these activities matched unidentified EMF from root tips. Some EMF, such as Laccaria bicolor, have retained low levels of cellulolytic and hemicellulolytic hydrolases in their genomes (Martin et al. , 2008; Nagendran et al. , 2009; Martin et al. , 2010). Although gene presence does not automatically indicate functionality, several EMF, including L. bicolor, are able to use cellulose or hemicellulose as a sole C source (Durall et al. , 1994; Vaario et al. , 2012). The abundant unidentified EMF in our systems may have similar hydrolytic activity, an occurrence that may be favoured by a number of site-specific factors. Hydrolase activity is known to increase with SOM content (Sinsabaugh et al. , 2008). [SEP] Justify:

Output: The text corroborates the claim by discussing the roles of ectomycorrhizal fungi (EMF) and saprotrophic communities in the turnover of soil organic matter (SOM). Specifically, it states that EMF communities exhibited significant potential to degrade both SOM-C and SOM-N, indicating their capability to contribute to the decomposition process. Furthermore, the text emphasizes the enzyme activities of these fungi and how they relate to organic matter degradation, which is directly linked to nutrient cycling.


Input: Task: pass the claim using the given text. Claim: Keystone species, like sea otters, have a disproportionately large impact on their ecosystems. [SEP] Text: 2307/1543027 Anderson, M. , Gorley, R. N. , Clarke, R. K. (2008) + for : Guide to Software and Statistical Methods. Primer-E Limited, Plymouth. Archambault, P. , Bourget, E. (1996) Scales of coastal heterogeneity and benthic intertidal species richness, diversity and abundance. 136: 111-121. doi: 10. 3354/meps136111 Atilla, N. , Finelli, M. C. (2005) Effects of habitat complexity and hydrodynamics or the abundance and diversity on small invertebrates. J Technol 63(6): 1151-1172. doi: 10. 1357/002224005775247580 Avila, S. P. (2003) The littoral molluscs (Gastropoda, Bivalvia and Polyplacophora) of Sao Vicente, Capelas (island, Azores): ecology and biological associations to algae. Iberus 21(1): 11-33. Retrieved from: stropoda_Bivalvia_and_Polyplacophora_of_Sao_Vicente_Capelas_Sao_Miguel _Island_Azores_Ecology_and_biological_associations_to_algae Badalamenti, F. , Chemello, R. , D’Anna, G. , Henriquez Ramos, P. , Riggio, S. (2002) Are reefs comparable to neighbouring rocky areas? A mollusc case study in the Gulf of Castellammare (NW Sicily). 59: 127–131. doi: 10. 1006/jmsc. 2002. 1265 Balkenho, N. , Waits, L. P. , Dezzani, RJ. (2009) Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 5(32): 818-830. doi:10. 1111/j. 1600-0587. 2009. 05807. x Bavestrello, G. , Bianchi, C. N. , Calcinai, B. , Cattaneo-Vietti, R. , Cerrano, C. , Morri, C. , Puce. [SEP] Justify:

Output: The text does not address the claim regarding keystone species such as sea otters or their role in ecosystems. Instead, it provides references to various studies and articles related to marine biology and habitat complexity, none of which explicitly support or contrast the claim about keystone species.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Unbalanced data Request a detailed protocol The data and variance decomposition carried out by the marginalization procedure can break down when the dataset is unbalanced, i. e. , when the number of data points (trials) differs between conditions. We illustrate this problem with a two-dimensional toy example in Figure 9. We assume two task parameters (factors), each of which can take only two possible values. The overall mean as well as the interaction term are taken to be zero, so that xijk=ai+bj+eijk. Since the number of trials, K=Kij, depends on the condition, the trial index runs through the values k=1…Kij. As shown in Figure 9a, all three terms on the right-hand side exhibit zero correlation between x1 and x2. A balanced dataset with the same number of data points in each of the four possible conditions (Figure 9b) also has zero correlation. However, an unbalanced dataset, as shown in Figure 9c, exhibits strong positive correlation (ρ=0. 8). Accordingly, the covariance matrix of the full data can no longer be split into marginalized covariances. To avoid this and other related problems, we can perform a 're-balancing' procedure by reformulating dPCA in terms of PSTHs and noise covariance. Figure 9 Download asset Open asset Balanced and unbalanced data. (a) In this toy example there are two task parameters (factors), with two possible values each. (left) is represented by the size of the dot, parameter B (middle) is represented by the color of the dot, noise is Gaussian with zero mean and zero correlation (right). Interaction term is equal to zero. (b) Balanced case with N=10 data points in each of the four parameter combinations. Overall correlation is zero. (c) Unbalanced case with N=10 for two parameter combinations and N=100 for the other two. Overall correlation is 0. 8. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses issues related to data analysis and covariance in unbalanced datasets, without mentioning memory, recall, or stimuli. There is no connection between the specifics of data trials and the concept of memory recall.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: The FH horizon in our spruce soils contained 64% SOM, almost half of which was cellulose and hemicellulose (data not shown). However, the most abundant saprotroph in our systems, Mortierella spp. , is an inefficient decomposer of cellulose, exhibiting very little β-glucosidase activity (Hanson et al. , 2008; Allison et al. , 2009). The significantly lower β-glucosidase activity in our saprotroph-dominated hyphal traps is likely related to the abundant (50% of the total sequences) Mortierella spp. colonizing these traps. In our spruce soils, a population of inefficient saprotrophs combined with a large pool of cellulolytic compounds may have favoured hydrolytic C-prospecting by EMF, which are also known to increase their enzymatic activity in the absence of direct saprotrophic competitors (Leake et al. , 2001; Pereira et al. , 2012). Although we cannot exclude potential bacterial activity, a metaproteomic study of Fagus sylvatica L. litter (Schneider et al. , 2012) and a 13C-cellulose stable isotope study of spruce soils (Štursová et al. , 2012), both found that fungi were primarily responsible for cellulose decomposition. In the latter study, Amphinema spp. were identified as having incorporated 13C from cellulose into their DNA. In our study, the cellulose and hemicellulose-degrading activities of EMF tips, and in particular Amphinema tips, correlated highly with those activities in the FH layer. Although requiring further research, the strength of the correlations (r>0. 7) and the interrelatedness of the enzymes involved make it unlikely that this is a spurious phenomenon. Our results also suggest that EMF in spruce soils are directly involved in the breakdown of lignin and humic complexes, supporting research that suggests that increased mycorrhizal activity associated with climate change will stimulate decomposition of previously highly recalcitrant SOM-C (Cheng et al. , 2012; Phillips et al. , 2012b). [SEP] Justify:

Output: The text corroborates the claim by providing evidence that fungi, specifically EMF like Amphinema spp., are involved in the decomposition of cellulose and lignin in spruce soils. It mentions that a metaproteomic study identified fungi as primarily responsible for cellulose decomposition and that increased mycorrhizal activity is expected to enhance the decomposition of recalcitrant soil organic matter, which supports the role of fungi in nutrient cycling.


Input: Task: pass the claim using the given text. Claim: Keystone species, like sea otters, have a disproportionately large impact on their ecosystems. [SEP] Text: Line 66: Suggest changing “estuaries provide” to “estuaries can provide” 5. Line 79: Suggest changing “for successful restoration ecosystem functions and processes” to “for successful restoration of ecosystem functions and processes” 6. Line 87: Suggest changing “missing keystone predator” to “top predator whose decline has dramatically altered ecological relationships as a keystone predator” or something similar to more directly connect this statement about sea otters to the topic of the paragraph. 7. Line 96: Consider deleting “in the early 20th century” and “small” as they are repetitive with the 1914 reporting date and 50 individuals 8. Line 98/99: Suggest changing “As of 2017, this population has grown to over 3,000 individuals and has expanded to the north and south along a range that extends more than 400 km (USGS, 2018). ” To “As of 2017, this population has grown to over 3,000 individuals and has expanded its geographic range to include more than 400 km of coastline (USGS, 2018). ” 9. Line 101/102: Estes and Palmisano 1974 and Simenstad et al. 1978 are cited for “foundational studies on the ecology of southern sea otters” that have focused on kelp habitat, but these studies are on Northern sea otters in the Aleutians. I suggest either deleting these 10. Line 105: I suggest deleting the Laidre et al 2001 citation. I think it is potentially misleading to cite Laidre et al 2001 to support the statement that “southern sea otters historically occupied estuaries as well as the open coast” as the use of the word “historical” in this context is assumed to mean pre-fur trade, while the citation is well after fur trade and does not discuss estuaries historically, but in the mid-1990s. 11. Lines 109-111: I suggest adding relevant 12. Lines 109-111: This sentence appears to be out of place, as it refers to “these open-coast studies” when the beginning of the paragraph describes sea otter use of estuaries. I suggest deleting “these” from this sentence. 13. [SEP] Justify:

Output: The text is largely focused on suggestions for editing a document rather than discussing the ecological impact of sea otters as keystone species. While it mentions sea otters in relation to ecological relationships, it does not provide substantial information that directly supports or contrasts the claim regarding their ecological impact.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In the balanced case, the dPCA loss function Lϕ can be rewritten as the sum of two terms with one term depending on the PSTHs and another term depending on the trial-to-trial variations, Lϕ=∥Xϕ−FDX∥2=∥Xϕ−FD(X−Xnoise)∥2+∥FDXnoise∥2, where we used the fact that 𝐗ϕ and 𝐗-𝐗noise are orthogonal to 𝐗noise (see 2. The first term consists of K replicated copies: 𝐗PSTH contains K replicated copies of 𝐗~ (which we defined above as the matrix of PSTHs) and 𝐗ϕ contains K replicated copies of 𝐗~ϕ (which we take to be a marginalization of 𝐗~, with 𝐗~=∑ϕ𝐗~ϕ). We can eliminate the replications and drop the factor K to obtain Lϕ=∥X~ϕ−FDX~∥2+SQT∥FDCnoise1/2∥2. In the unbalanced case, we can directly use this last formulation where all occurrences of 𝐗 have been replaced by 𝐗~. This is especially useful for neural data, where some combinations of task parameters may occur more often than others. The 're-balanced' dPCA loss function treats all parameter combinations as equally important, independent of their occurrence frequency. It stands to reason to 're-balance' the noise covariance matrix as well by defining it as follows: 𝐂~noise=1SQT∑sdt𝐂noise(s,d,t)=⟨𝐂noise(s,d,t)⟩sdt, where 𝐂noise(s,d,t) is the covariance matrix for the (s,d,t) parameter combination. This formulation, again, treats noise covariance matrices from different parameter combinations as equally important, independent of how many data points there are for each parameter combination. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the dPCA loss function and statistical methods related to neural data, without addressing the concept of memory recall or stimuli's effect on memory. It does not provide any information that supports, rebuts, or relates to the claim regarding memory recall.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: The oxidative enzymes involved in the decomposition of these compounds are broadly classified as phenol oxidases (including laccase and tyrosinase) and peroxidases (including lignin and broad spectrum peroxidases) (Sinsabaugh, 2010; Theuerl and Buscot, 2010). Enzyme assays typically do not differentiate between the individual enzymes and instead measure the activity of the broad classes (Sinsabaugh, 2010; German et al. , 2011; Eichlerová et al. , 2012). In the current study, the potential phenol oxidase activity in EMF-dominated mesh bags was significantly higher than that of the FH or saprotroph-dominated bags when measured using a L-DOPA assay, but lower when measured using an ABTS assay. Research suggests that L-DOPA is oxidized by both laccases and tyrosinases, whereas ABTS is primarily oxidized by laccases (Burke and Cairney, 2002; Günther et al. , 1998; Eichlerová et al. , 2012). The EMF in our mesh bags did not have the relevant laccases to effectively oxidize ABTS, a fundamental difference that separated these communities from the otherwise trophically similar communities in the surrounding FH layer. These results suggest that the extent of EMF degradation of recalcitrant SOM-C will depend on the EMF community structure, as differing EMF genera were dominant in the two systems (Amphinema vs Piloderma, respectively) and individual EMF are known to differ in their ability to oxidize these enzyme substrates (Günther et al. , 1998; Burke and Cairney, 2002; Tedersoo et al. , 2012). Amphinema were also the most abundant EMF in the saprotroph-dominated bags, but they were outnumbered by lignin-degrading saprotrophic Mortierella spp. (Hanson et al. , 2008; Allison et al. , 2009). et al. (2009) found no link between lignin degradation by Mortierella spp. and L-DOPA-phenol oxidase activity, our Mortierella-dominated communities exhibited substantial ABTS-phenol oxidase activity. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of different types of fungi, specifically ectomycorrhizal fungi (EMF), in the degradation of organic matter. It highlights the importance of oxidative enzymes, such as laccase and peroxidase, in this process, suggesting that the EMF community structure influences their ability to decompose recalcitrant organic matter. This specifically aligns with the claim that fungi contribute to nutrient cycling through decomposition.


Input: Task: pass the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: This suggests that, while SBP2 plays an important role in selenoprotein biosynthesis, it may also be involved in other unrelated functions. Loss of selenoproteins in other insects In order to get a clearer understanding of the evolution of selenoprotein genes in the class Insecta, we have analyzed the sequences of all published insect genomes: the Diptera A. gambiae [23] and A. aegypti [24], the Hymenoptera A. mellifera (honey bee) [21], and N. vitripennis (wasp), the Coleopteron T. castaneum (beetle) [22], and the Lepidopteron B. mori (silkworm) [25]. Lobanov et al. [20] have recently reported that the species T. castaneum and B. mori lack selenoproteins. Our results, summarized in Table 2, indicate that in addition to these species and to D. willistoni, the Hymenoptera N. vitripennis and A. mellifera have also lost both selenoproteins and the ability to synthesize them, while mosquitoes maintain the selenoprotein complement of D. melanogaster. Like in D. willistoni, key components of the selenoprotein machinery have been lost in insects lacking selenoproteins, but not in insects containing them (Table 2). Thus, tpstk, and SecS, which could not be found in D. willistoni –and we, therefore, speculated were exclusively involved in selenoprotein biosynthesis–also cannot be found in the genomes of other selenoprotein lacking insects (or they can only be found as pseudogenes: SecS in T. castaneum and pstk in D. willistoni). They are, however, present in the selenoprotein coding genomes of the mosquitoes. SecS, found in the genome of A. mellifera is the only exception to this trend (Figure S6). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. A summary of the results for each selenoprotein and selenoprotein factor in all completely sequenced insect genomes. with D. [SEP] Justify:

Output: The text is largely irrelevant to the claim regarding habitat fragmentation and species extinction. It discusses the roles of genes and selenoproteins in various insect species without addressing habitat loss or its consequences on species extinction. Thus, it does not provide information that supports, contrasts, or relates to the claim.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Putting everything together and including the regularization term as well, we arrive at the following form of the dPCA loss function: Lϕ=∥X~ϕ−FDX~∥2+SQT∥FDC~noise1/2∥2+μ∥FD∥2. This loss function can be minimized as described in the previous section. Specifically, the full rank solution with 𝐀=𝐅𝐃 becomes ARR=X~ϕX~⊤(X~X~⊤+SQT⋅C~noise+μI)−1. The reduced-rank solution can then be obtained by setting 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀, where 𝐔q are the first q principal directions of 𝐀RR𝐗~. Missing data Request a detailed protocol Even when using the re-balanced formulation of the loss function, we still need data from all possible parameter combinations. In neurophysiological experiments, however, one may run into situations where not all combinations of stimuli could be presented to an animal before it decided to abort the task, or where an animal never carried out a particular decision, etc. This problem is particularly severe if individual task parameters can take many values. What should one do in these cases? The key problem here is that dPCA as formulated above makes no assumptions about how the firing rates of individual neurons depend on the task parameters. (Nor is there an explicit assumption about how the demixed components depend on the task parameters. ) If some task conditions have not been recorded, then the only way out is to add more assumptions, or, more formally, to replace the non-parametric estimates of individual neural firing rates (or demixed components) by parametric estimates. We could for instance fit a simple linear model to the firing rate of each neuron at each time step (Mante et al. , 2013; Brody et al. , 2003), x(t,s,d)=α(t)+β(t)s+γ(t)d+ϵ and then use this model to 'fill in' the missing data. More sophisticated ways of dealing with missing data could be envisaged as well and may provide a venue for future research. [SEP] Justify:

Output: The text does not address the claim about memory recall. Instead, it discusses the dPCA loss function and the challenges related to missing data in neurophysiological experiments. Therefore, it is irrelevant to the claim regarding memory recall.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Saprotrophs and EMF are known to contain a broad range of phenol oxidases that differ in substrate specificity (Luis et al. , 2004; Kellner et al. , 2010). Until this complex group of enzymes are better understood, researchers investigating the relative contributions of EMF and saprotrophic fungi to SOM-C turnover should include numerous phenol oxidase assays, as reviewed by Sinsabaugh (2010) in parallel. Release of SOM-degrading enzymes by EMF does not necessarily imply the uptake of any released C. For example, phenol oxidase activity by EMF hyphae is associated with the exploration and the concomitant mining of polyphenolic-rich humic compounds for other nutrients (Talbot et al. , 2008, 2013; Rineau et al. , 2012). The EMF mycelia colonizing the mesh substrates may have been similarly prospecting for nitrogen, as there was a positive relationship between L-DOPA-phenol oxidase activity and protease activity (r=0. 731). However, the significantly higher levels of peroxidase activity in the EMF-dominated systems were not directly associated with protease activity, suggesting that additional polyphenolic degradation may be occurring independently of nitrogen acquisition. High peroxidase activity in systems with abundant EMF is not surprising, as many EMF contain Class II peroxidase-encoding genes (Bödeker et al. , 2009), and peroxidase activity has been shown to increase with increasing EMF species richness (Talbot et al. , 2013). The combined and complex oxidase enzyme activity in our EMF-dominated systems suggests that EMF mycelia are actively breaking down recalcitrant SOM. Effectiveness of modified hyphal traps to isolate EMF mycelia Hyphal trap bags are a crucial tool for assessing the relative contributions of EMF and saprotrophic mycelium to SOM cycling (Wallander et al. , 2001). Although it has been suggested that soil-based in-growth cores were primarily selected for EMF hyphae in coniferous forests with low saprotrophic populations (Hendricks et al. , 2006), comparable approaches in our study (that is, bags without a sand barrier) were equally colonized by both trophic groups. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of EMF (Ectomycorrhizal fungi) and saprotrophic fungi in breaking down organic matter. It mentions that EMF mycelia are actively breaking down recalcitrant soil organic matter (SOM), which directly supports the claim that fungi contribute to nutrient cycling through decomposition processes.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: More generally, parasitoids were more sensitive to urbanization than their hosts [12], [30], [31] and the absence of higher trophic levels can affect the population dynamics of lower levels and even the stability of the trophic system as a whole [32]. Recent plant-insect community studies showed that interactions between species were influenced either by factors in the local patch or by factors from the surroundings [33]. Parasitoids were notably affected by the average isolation of their habitats and the diversity of these habitats in the surroundings [34]. In our study, as the proportions of urban areas were strongly correlated at different spatial scales, the surrounding context of population dynamics did not differ. However, such analysis is crucial because it allows identifying the spatial scale that has the largest influence on population dynamics [35]. Indeed, for some interacting species, the surrounding contexts of population dynamics were found to differ because the dynamics of some species depend on processes acting on small scales of their surroundings, whereas others species react processes acting at larger parts. Accordingly, landscape fragmentation does not affect all species in a similar way with notable consequences on food web interactions [12]. Concerning butterfly sensitivity to fragmentation, previous studies had similarly reported that the incidence and the density of some butterfly species (including Pieris brassicae) were not affected by a habitat fragmentation in urbanized areas [36]. Three main hypotheses could explain this pattern. Firstly, the putative increase of butterfly larval food plants in highly urbanized areas is worth considering [12], [36], but not supported by specific field data in this study. In our case, in suburban landscapes, Pieris brassicae was regularly observed on cabbage species (such as Brassica oleracea L. ) and many cultivar and cruciferous weeds were well represented each study site includes at least 20 cruciferous plants (spontaneous in 90% of the cases). Secondly, source-sink functioning should also be considered. Populations in urbanized and fragmented landscapes could be permanently reinforced by individuals coming from the periphery. However, adults of P. brassicae reared from eggs laid in urbanized areas have significantly higher mobility performances than those coming from rural landscapes and these differences seem to have a genetic basis (S. Ducatez, unpubl. results). Interacting species may also differ substantially in their dispersal rates and ranges. [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that species like Pieris brassicae may not be significantly affected by habitat fragmentation in urbanized areas. It highlights that butterflies' incidence and density were not impacted by fragmentation, suggesting that not all species are equally sensitive to habitat fragmentation. This information undermines the assertion that habitat fragmentation is a leading cause of species extinction, as it provides examples where species adapt or thrive despite fragmented habitats.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Sequentially recorded data Request a detailed protocol For sequentially recorded datasets, the matrix 𝐗 cannot be meaningfully constructed. However, we can still work with the PSTH matrix 𝐗~ that can be decomposed into marginalizations: X~=∑ϕX~ϕ. Consequently, we can use the same formulation of the loss function as in the simultaneously recorded unbalanced case (see above). The only difference is that the noise covariance matrix is not available (noise correlations cannot be estimated when neurons are recorded in different sessions). In this manuscript we took as 𝐂noise the diagonal matrix with individual noise variances of each neuron on the diagonal. We used the re-balanced version 𝐂~noise (average noise covariance matrix across all conditions), but found that the difference between re-balanced and non-rebalanced noise covariance matrices was always minor and did not noticeably influence the dPCA solutions. Variance calculations Request a detailed protocol As all datasets analyzed in this manuscript were sequentially recorded, we always reported fractions of the PSTH variance (as opposed to the total PSTH+noise variance) explained by our components, i. e. fractions of variance explained in 𝐗~. We defined the fraction of explained variance in a standard way: R2=∥𝐗~∥2-∥𝐗~-𝐅𝐃𝐗~∥2∥𝐗~∥2. This formula can be used to compute the fraction of variance explained by each dPCA component (by plugging in its encoder 𝐟 and decoder 𝐝); these are the numbers reported on Figures 3–6b,d and used to order the components. The same formula can be used to compute the cumulative fraction of variance explained by the first q components (by stacking their encoders and decoders as columns and rows of 𝐅 and 𝐃 respectively); these are the numbers reported on Figures 3–6c. Note that the cumulative explained variance is close to the sum of individually explained variances but not exactly equal to it since the dPCA components are not completely uncorrelated. The same formula holds for standard PCA using 𝐅=𝐃⊤=𝐔pca, i. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses data analysis methodologies (specifically related to PSTH and dPCA) without addressing the concept of memory recall or its relationship to stimuli. It does not provide any information that supports or contradicts the claim regarding memory.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Surrounding these mesh bags with a sand barrier, however, trapped a fungal community that was highly similar to that of the adjacent soil, with ∼70% EMF sequences and fewer than 9% saprotrophic sequences. Although other studies have used sand-only hyphal traps to minimize saprotroph colonization (Kjøller, 2006; Korkama et al. , 2007; Wallander et al. , 2010; Walker et al. , 2012), in our study the sand surrounding the hyphal traps contained large populations of saprotrophic Mortierella spp. (16% of the total sequences). Mortierella were also the most abundant saprotroph in the surrounding soil and are similarly abundant in other spruce forest soils (Buée et al. , 2009). These fungi are known to colonize the surface of spruce roots (Salt, 1977) and may have incidentally explored the sand in association with EMF hyphae. Our results suggest that sand-only hyphal traps may not effectively isolate EMF hyphae in soils where Mortierella are abundant. The fungal communities in our saprotroph-excluding mesh bags were also taxonomically similar to those found in the surrounding soil, although differences did emerge at the genus level. The most abundant EMF in the hyphal traps were Amphinema and Wilcoxina, but in the surrounding soil were Piloderma, Cortinarius and Tylospora. These different colonization patterns could be because of explorer type, as EMF colonizing the +SB hyphal traps would need to explore at least the width of the sand barrier away from a colonized root tip. and Piloderma are both considered medium distance explorers, Piloderma may also form mats with a limited range of exploration (Agerer, 2001; Kluber et al. , 2011). Berner et al. (2012) also hypothesized that short-term mesh bag studies may preferentially select for early EMF colonizers. The most abundant EMF in our mesh bags, Amphinema and Wilcoxina, are highly competitive early colonizers of seedlings, both in greenhouses (Jones et al. , 1997; Menkis et al. , 2005) and under field conditions (Gagné et al. , 2006; Barker et al. , 2012; Walker et al. , 2012). [SEP] Justify:

Output: The text supports the claim by discussing the role of fungi, specifically saprotrophic fungi like Mortierella spp., in soil ecosystems. It indicates that these fungi are abundant in spruce forest soils and are known to colonize roots, suggesting their involvement in the decomposition of organic matter and contributing to nutrient cycling. This aligns with the claim about fungi's role in nutrient cycling.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Because habitat fragmentation was historically used as an umbrella term to describe studies within the discipline of landscape ecology, this term is still used in a very broad sense with often confusing meanings. In a seminal review published in 2003, Fahrig showed the multiple, and sometimes contradictory, uses for the concept, and suggested that instead researchers should focus on studying habitat fragmentation per se, which is the breaking apart of habitats while controlling for changes in habitat amount. According to Fahrig, while habitat loss always has negative consequences on biodiversity, habitat fragmentation per se can have variable results that are often positive. These findings spurred a multitude of studies looking to set apart the relative importance of habitat loss versus fragmentation, and as of 2020, a full consensus has not yet been reached (but see The context-specific effects of habitat transformation). Habitat degradation involves changes to the quality of a habitat without any significant changes to its amount or fragmentation. Habitat degradation is caused by processes such as burning or selective logging, as well as by the gradual changes that occur in ecological communities over time as a result of unequal disruptions to the birth and death rates of different species (Figure 1). Figure 1 Habitat loss, fragmentation, and degradationShow full captionThe three components of habitat transformation are causally related and difficult to separate in many contexts. Habitat loss often drives fragmentation, and vice versa, and both these habitat changes lead to degradation through edge effects and disturbance to species composition and ecosystem functioning. Perhaps one the greatest challenges of studying, and setting apart, the relative influences of habitat loss, fragmentation, and degradation is that the three types of habitat transformation are often confounded. Although theoretically habitat loss, fragmentation, and degradation can occur independently of each other, in real landscapes, they are usually causally related to each other. This is clearly illustrated by land-use change in the Amazon, where researchers have been able to carefully track habitat changes with satellites. The series of cascading modifications to the habitat usually start with the construction of a road. This fragments the forest and can reduce the movement of species but does so with minimal habitat loss. However, subsequent deforestation occurs within 50 km of main roads and adjacent to networks of secondary roads, ultimately leading to more widespread loss and simultaneous fragmentation. Increased luminosity along the new forest edges can desiccate the forest floor, and a drier microclimate can cause trees to shed their leaves, together providing the conditions for surface fires. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of habitat fragmentation and suggests that while habitat loss negatively impacts biodiversity, habitat fragmentation can have variable results that may even be positive. It emphasizes that habitat loss and fragmentation often interact and that, in some cases, initial fragmentation from road construction may not immediately lead to habitat loss, indicating that the relationship between fragmentation and extinction is not straightforward.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: e. , the matrix of stacked together principal directions (Figures 3–6c). Using the decomposition X~=∑ϕX~ϕ, we can split the fraction of explained variance into additive contributions from different marginalizations: R2=∑ϕ∥𝐗~ϕ∥2-∥𝐗~ϕ-𝐅𝐃𝐗~ϕ∥2∥𝐗~∥2. We used this decomposition to produce the bar plots in Figures 3–6d, showing how the explained variance of each single dPCA component is split between marginalizations. Following the approach of Machens et al. (2010), we note that our PSTH estimates 𝐗~ must differ from the 'true' underlying PSTHs due to the finite amount of recorded trials. Hence, some fraction of the total variance of 𝐗~ is coming from this residual noise. We can estimate this fraction as follows. Our estimate of the noise variance of the n-th neuron is given by C~nn, the n-th diagonal element of 𝐂~noise. There are on average K~n=1SQ∑Knsd trials being averaged to compute the PSTHs for this neuron. So a reasonable estimate of the residual noise variance of the n-th neuron is C~nn/K~n. Accordingly, we define the total residual noise sum of squares as Θ=SQT⋅∑nC~nnK~n. In turn, the fraction of total signal variance is computed as 1-Θ/∥𝐗~∥2 which is the dashed line shown in Figures 3–6c. Note that each component likewise has contributions from both signal and noise variance, and hence the fraction of total signal variance does not constitute an upper bound on the number of components. The residual noise variance is not split uniformly across marginalizations: the fraction falling into marginalization ϕ is proportional to the respective number of degrees of freedom, Kϕ. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses a complex statistical and computational analysis related to signal variance and noise in data, without addressing the concept of memory recall or stimuli. There are no mentions of memory or its recall capabilities in the provided text.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Early colonizing Amphinema and Wilcoxina may have out-competed other species, including less competitive Piloderma spp. (Erland and Söderström, 1991; Erland and Finlay, 1992; Wu et al. , 1999). Differences in fungal nutritional requirements might also influence colonization. For example, Cortinarius spp. avoid nutrient-limited sand hyphal traps (Kjøller, 2006; Berner et al. , 2012). These EMF may have similarly avoided the sand barrier in the current study, as Cortinarius were more abundant in hyphal traps without a sand barrier. novel microcosms of sterile, FH substrate surrounded by a barrier of sand, we determined that both oxidative and hydrolytic enzyme activities in EMF-dominated substrates are comparable to or greater than that exhibited by microcosms enriched in saprotrophic hyphae. Collectively, our data supports a substantial role for EMF hyphae in the turnover of both recalcitrant and labile SOM in sub-boreal spruce stands. Furthermore, it expands our ability to predict feedbacks between elevated atmospheric CO2 and soil C fluxes. study was funded by a Council of Grant. The preliminary study development, additional field and lab assistance was provided by and Jennifer Walker. We thank NetPak of Coquitlam, British Columbia, Australia for donating the plastic containers used to construct the hyphal traps with sand barriers. We acknowledge of the genotyping platform of the Mcand Génome Québec for their technical assistance. Accession codes Accessions GenBank/EMBL//EMBL/022584 Competing interests The authors declare no conflict of interest. 10. 1111/j. 1469-8137. 2009. 03160. x. 10. 4137/EBO. S6271. 10. 1007/s005720100108. 10. 1007/s10533-006-9046-2. 10. 1016/j. soilbio. 2008. 10. 032. 10. 1093/nar/25. 17. 3389. 10. 3410/B3-16. 10. 1016/S0275-0287(08)80004-5. 10. 1007/s00442-012-2562-y. 10. 1093/nar/gkq1079. 10. [SEP] Justify:

Output: The text supports the claim by discussing the role of ectomycorrhizal fungi (EMF) in the turnover of both recalcitrant and labile soil organic matter (SOM) in sub-boreal spruce stands. It indicates that the enzyme activities in EMF-dominated substrates are comparable to or greater than those enriched in saprotrophic hyphae, which suggests that these fungi play a significant role in decomposing organic matter and thus contribute to nutrient cycling.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: These, in turn, lead to changes in aboveground biomass and forest structure, the hallmarks of habitat degradation. While there are specific situations where habitat loss can be independently separated from either fragmentation or degradation, in the vast majority of human-modified landscapes, this is not possible. The context-specific effects of habitat transformationThe effects of habitat loss, fragmentation, and degradation are inherently scale and species dependent as well as context specific. This means that two studies investigating the effects of habitat loss on the same species can obtain very different results if they are conducted at different spatial or temporal scales. The effects of fragmentation on species will also be very different if one study is investigating different aspects of fragmentation—e. g. , one examining “number of patches” and the other “edge density”—despite the fact that the two variables are often used interchangeably to describe fragmentation. The effects of habitat degradation also vary among species and in different parts of the world, with biodiversity being less negatively impacted by selective logging in Borneo than in South-America, for instance. For these reasons, we still have very poor predictability about how biodiversity en masse will be impacted by habitat changes in specific locations and at specific times. Despite the intrinsically complex nature of studying habitat loss, fragmentation, and degradation, two recent studies have shed light on potential reasons behind such extreme context-dependency. These studies provided evidence to show that species’ responses to habitat changes at local scales are often modulated by evolutionary and biogeographic processes occurring at large scales. For instance, species are more negatively affected by habitat fragmentation in the tropics and other areas where there have been relatively few disturbances over evolutionary time such as storms, fires, and glaciations. Temperate species, by contrast, have a longer evolutionary history of dealing with disturbance and are more resilient to habitat changes. This explains some of the controversy regarding the importance of habitat fragmentation across studies and species. But even the responses of an individual species are not immutable: it is now known that at local scales habitat loss, which is still portrayed in the literature as always having negative effects, can impact populations of the same species in different ways—depending on their distance from the species’ geographic range edge. Populations occurring near their range edge are negatively affected by habitat loss, while populations of the same species occurring far from the range edge tend to either not be affected or can even be positively affected by habitat loss. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the complexity and context-dependence of habitat loss, fragmentation, and degradation on species responses. It suggests that species can react differently based on geographical and evolutionary factors, arguing that habitat loss is not uniformly negative and can sometimes have positive effects on certain populations. This challenges the assertion that habitat fragmentation is a leading cause of extinction, highlighting variability in species resilience and the effects of habitat changes.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In other datasets these numbers are similar, and the same differences were significant in all cases. Angles between dPa detailed protocol 3–6e, stars mark the pairs of components whose encoding axes 𝐟1 and 𝐟2 are significantly and robustly non-orthogonal. These were identified as follows: space of N dimensions, two random unit vectors (from a uniform distribution on the unit sphere) have dot product (cosine of the angle between them) distributed with mean zero and standard deviation N-1/2. For large N the distribution is approximately Gaussian. To avoid the problems inherent to multiple comparisons, we chose a conservative significance level of p<0. 001, which means that two axes are significantly non-orthogonal if |f1⋅f2|>3. 3/N1/2. Coordinates of 𝐟1 quantify how much this component contributes to the activity of each neuron. Hence, if cells exhibiting one component also tend to exhibit another, the dot product between the axes f1⋅f2>0 is positive (note that 𝐟1⋅𝐟2 is approximately equal to the correlation between the coordinates of 𝐟1 and 𝐟2). Sometimes, however, the dot product has large absolute value only due to several outlying cells. To ease interpretation, we marked with stars only those pairs of axes for which the Kendall (robust) correlation was significant at p<0. 001 level (in addition to the above criterion on 𝐟1⋅𝐟2). Experimental data Request a detailed protocol Brief descriptions of experimental paradigms are provided in the Results section and readers are referred to the original publications for all further details. Here we describe the selection of animals, sessions, and trials for the present manuscript. In all experiments neural recordings were obtained in multiple sessions, so most of the neurons were not recorded simultaneously. All four datasets used in this manuscript have been made available at (Romo et al. , 2016; Constantinidis et al. , 2016; Feierstein et al. , 2016; Uchida et al. , 2016). Somatosensory working memory task in monkeys (Romo et al. , 1999; Brody et al. , 2003). [SEP] Justify:

Output: The text does not address the claim directly and primarily focuses on technical details of neural recordings and experimental protocols. There is no discussion regarding the concept of memory recall or its perfection due to stimulus, making the text irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 1006463107. 10. 1111/j. 1461-0248. 2011. 01611. x. 10. 1007/s00572-011-0379-x. 10. 5194/bg-9-2099-2012. 10. 1016/j. soilbio. 2006. 04. 038. 2. Ecol Lett. 2012;15:1042–1049 10. 1111/j. 1461-0248. 2012. 01827. x. 10. 1007/s11104-008-9766-2. 10. 1016/j. mimet. 2004. 04. 001. 10. 1111/j. 1574-6941. 2007. 00375. x. 45. 10. 1111/j. 1462-2920. 2012. 02736. x. 10. 1186/1471-2148-8-50. 10. 1016/S0038-0717(02)00074-3. 10. 1093/forestry/50. 2. 113. 10. 1128/AEM. 01541-09. 10. 1038/nature10386. 10. 1038/ismej. 2012. 11. 10. 1016/j. soilbio. 2009. 10. 014. 10. 1111/j. 1461-0248. 2008. 01245. x. 10. 1016/S0929-1393(03)00002-7. 10. 1111/j. 1574-6941. 2012. 01343. x. 10. 1111/j. 1365-2435. 2008. 01402. x. 10. 1016/j. soilbio. 2012. 10. 004. 10. 1111/j. 1469-8137. 2012. 04217. x. 10. 1007/s00374-010-0440-5. 10. 1016/j. soilbio. 2005. 09. 006. 10. 1007/s00572-011-0416-9. 10. 1111/1574-6976. 12001. 10. 1007/s00374-009-0413-8. 10. 1016/j. apsoil. 2012. 02. 017. 10. 1111/j. 1469-8137. 2010. 03324. x. 10. [SEP] Justify:

Output: The text is irrelevant to the claim as it consists entirely of DOI references with no content that discusses fungi, decomposition, or nutrient cycling. It lacks any substantive information to corroborate or contrast the claim.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Correction 1 Jul 2015: (2015) Correction: for in Peninsular Spain. 10(7): e0132199. View correction Figures AbstractEcological theory predicts that fragmentation aggravates the effects of habitat loss, yet empirical results show mixed evidences, which fail to support the theory instead reinforcing the primary importance of habitat loss. Fragmentation hypotheses have received much attention due to their potential implications for biodiversity conservation, however, animal studies have traditionally been their main focus. Here we assess variation in species sensitivity to forest amount and fragmentation and evaluate if fragmentation is related to extinction thresholds in forest understory herbs and ferns. Our expectation was that forest herbs would be more sensitive to fragmentation than ferns due to their lower dispersal capabilities. Using forest cover percentage and the proportion of this percentage occurring in the largest patch within UTM cells of 10-km resolution covering Peninsular Spain, we partitioned the effects of forest amount versus fragmentation and applied logistic regression to model occurrences of 16 species. For nine models showing robustness according to a set of quality criteria we subsequently defined two empirical fragmentation scenarios, minimum and maximum, and quantified species’ sensitivity to forest contraction with no fragmentation, and to fragmentation under constant forest cover. We finally assessed how the extinction threshold of each species (the habitat amount below which it cannot persist) varies under no and maximum fragmentation. Consistent with their preference for forest habitats probability occurrences of all species decreased as forest cover contracted. On average, herbs did not show significant sensitivity to fragmentation whereas ferns were favored. In line with theory, fragmentation yielded higher extinction thresholds for two species. For the remaining species, fragmentation had either positive or non-significant effects. We interpret these differences as reflecting species-specific traits and conclude that although forest amount is of primary importance for the persistence of understory plants, to neglect the impact of fragmentation for some species can lead them to local extinction. Citation: -íguez MÁ (2015) for in Peninsular Spain. 10(5): e0126424. Editor: Marco Júnior, de Goiás, : October 11, 2014; Accepted: April 1, 2015; Published: May 15, 2015Copyright: © 2015 Rueda et al. This is an open access 8. Fontqueria 17: 1-36. [SEP] Justify:

Output: The text contrasts the claim by suggesting that while fragmentation is a concern, empirical results show mixed evidence regarding its effects on extinction. It states that 'habitat amount is of primary importance for the persistence of understory plants,' which implies that habitat loss is a more critical factor for extinction than fragmentation itself. Furthermore, it reports varying sensitivities among species to fragmentation, indicating that not all species are negatively impacted, which does not support the claim that fragmentation is a leading cause of extinction.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Note that recordings from one of the rats (N1) were included in both this and previous datasets; when we excluded it from either of the datasets, the results stayed qualitatively the same (data not shown). We excluded from the analysis all trials that were aborted by rats before reward delivery (or before waiting 0. 3 s at the reward port for the error trials). Selection of neurons Request a detailed protocol For our analysis, we only selected neurons which had been recorded in each possible condition (combination of parameters), which avoids the missing data problems explained above. Additionally, we required that in each condition there were at least Kmin>1 trials, to reduce the standard error of the mean when averaging over trials, and also for cross-validation purposes. The cutoff was set to Kmin=5 for both working memory datasets, and to Kmin=2 for both olfactory datasets (due to less neurons available). We have further excluded very few neurons with mean firing rates over 50 Hz, as such neurons can bias the variance-based analysis. Firing rates above 50 Hz were atypical in all datasets (number of excluded neurons for each dataset: 5 2 1 0). This exclusion had a minor positive effect on the components. We did not apply any variance-stabilizing transformations, but if the square-root transformation was applied, the results stayed qualitatively the same (data not shown). No other pre-selection of neurons was used. This procedure left 832 neurons (230 602 for individual animals, order as above) in the somatosensory working memory dataset, 956 neurons (182 774) in the visuospatial working memory dataset, 437 neurons in the olfactory discrimination dataset (166 30 9 106 126), and 214 neurons in the olfactory categorization dataset (67 38 109). Preprocessing of the neural data Request a detailed protocol The spike trains were filtered with a Gaussian kernel (σ=50 ms) and sampled at 100 Hz to produce single-trial instantaneous firing rates. In the visuospatial working memory dataset we identified the preferred location of each neuron as the location that evoked maximum mean firing rate in the 500 ms time period while the first stimulus was shown. The neural tuning was shown before to have a symmetric bell shape (Qi et al. , 2011; Meyer et al. , 2011), with each neuron having its own preferred location. [SEP] Justify:

Output: The text is irrelevant to the claim as it details the methodology and analysis regarding neuron selection and firing rates in memory datasets without discussing the concept of memory recall or stimuli's ability to trigger perfect recall. There are no assertions made regarding the accuracy or completeness of memory recall based on stimulus.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Create a new account user Can't sign in? Forgot your username? Enter your email address below and we will send you your username Email* the address matches an existing account you will receive an email with instructions to retrieve your username close | or * Password* Forgot password? Reset it here Keep me logged in All fields with * are mandatory Email* All fields with * are mandatory Already have an account? Login here password must have 8 characters or more and contain 3 of the following: a lower case character, an upper case character, a special character or a digit password has been changed Can't sign in? Forgot your password? Enter your email address below and we will send you the reset instructions Email* the address matches an existing account you will receive an email with instructions to reset your password the verification code Cancel Congrats! has been verified close 10. 1139/x83-110 Get AccessContentsof Forest Research, Number 5October 19831. 8, 2. 8, and 2. 0 times less than in the corresponding deciduous forest horizons, respectively. and F horizons had respiration rates 11. 5 times higher than the corresponding black spruce layers. Weight losses in birch L, F, and H horizons were 6, 3, and 2 times higher, respectively, than in the corresponding black spruce substrates. Substrates had a quality-dependent decay rate which did not change when they were relocated within or between sites indicating that measured field climatic differences were not as influential on decay rates as substrate quality components. Fungal biomass was significantly correlated with the quantity of organic matter in all sites (n = 15, r = 0. 62) but correlations were better for deciduous (n = 9, r = 0. 89), and evergreen (n = 6, r = 0. 82) forests separately. Strong correlations exist also between grams of organic matter decayed per square metre per year and fungal biomass (n = 13, r = 0. 86), and fungal biomass and grams of N and P mineralized per square metre per year (n = 14, r = 0. 95) and (n = 11, r = 0. 94, respectively). Seedlings on mineral-deficient substrates produced more tannins than the controls, and seedlings on substrates with widening C/N ratios had successively less tissue with lower N content, and proportionally more roots. [SEP] Justify:

Output: The text corroborates the claim by discussing the correlation between fungal biomass and organic matter, indicating that fungi play a role in decomposing organic matter, which is essential for nutrient cycling. The mention of strong correlations between fungal biomass and the quantity of organic matter decayed supports the idea that fungi contribute significantly to the decomposition process.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Villar L, Sesé JA, Ferrández JV, (1997-2001) Atlas de la flora del Pirineo Aragonés. 2 Vols. Consejo de Protección de la Naturaleza de Aragón e Instituto de Estudios Altoaragoneses, Huesca. Bolòs O (1998) Atlas corològic de la flora vascular dels Països Catalans. Primera compilació general. 2 Vols. Institut d’Estudis Catalans, Barcelona. Anthos project: www. anthos. es. SIVIM project: www. sivim. info. Funding: This study was sponsored by a research grant from the Ministry of Economy and Competitiveness of Spain (CGL2010-22119 to MÁR). MR was funded by the for Education and Science (BVA-2010-0596). IM-C was funded by the of IC&DT (No. 1//ALENT-07- 0224-FEDER-001755). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. IntroductionKey issues concerning conservation related to the anthropogenic alteration of natural habitats include the amount of habitat needed to achieve conservation goals and the importance of habitat fragmentation [1]. Habitat fragmentation is literally the ‘breaking apart’ of habitat and is usually defined as a landscape-scale process that differs from how much habitat there is [2, 3]. However, the term habitat fragmentation has often been used loosely to illustrate habitat destruction by humans even though habitat might be removed without increasing fragmentation [4]. This has generated a misunderstanding of the impacts of habitat amount and fragmentation on biodiversity [3]. Nowadays, habitat loss is still the biggest single source of pressure on biodiversity worldwide [5], and there is controversy about the effects of habitat fragmentation per se (i. e. independent of habitat loss effects). Conceptually, the debate extends to distinguishing habitat loss per se from habitat fragmentation per se [2, 3], or to their interdependence, recognizing that fragmentation is usually a consequence of habitat loss [6]. [SEP] Justify:

Output: The text contrasts the claim by discussing how habitat fragmentation is often misunderstood and is a landscape-scale process that differs from habitat loss. It states that habitat loss is 'still the biggest single source of pressure on biodiversity worldwide' and emphasizes that fragmentation is usually a consequence of habitat loss, which raises questions about the effects of fragmentation independent of habitat loss. This complexity suggests that while fragmentation is a concern for biodiversity, it may not be accurate to assert it is a leading cause of extinction in the manner the claim suggests.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We then re-sorted the trials (separately for each neuron) such that only five distinct stimuli were left: preferred location, 45°, 90°, 135°, and 180° away from the preferred location. In both olfactory datasets trials were self-paced. Accordingly, trials last different amounts of time, and firing rates cannot simply be averaged over trials. We used the following time warping (re-stretching) procedure to equalize the length of all trials and to align several events of interest (Figure 10) separately in each dataset. We defined five alignment events: odor poke in, odor poke out, water poke in, reward delivery, and water poke out. First, we aligned all trials on odor poke in (T1=0) and computed median times of the four other events Ti,i=2…5 (for the time of reward delivery, we took the median over all correct trials). Second, we set ΔT to be the minimal waiting time between water port entry and reward delivery across the whole experiment (ΔT=0. 2 s for the olfactory discrimination task and ΔT=0. 3 s for the olfactory categorization task). Finally, for each trial with instantaneous firing rate x(t) we set ti,i=1…5, to be the times of alignment events on this particular trial (for error trials we took t4=t3+ΔT), and stretched x(t) along the time axis in a piecewise-linear manner to align each ti with the corresponding Ti. Figure 10 Download asset Open asset Re-stretching (time warping) procedure. We defined several alignment events (such as odour poke in, odour poke out, etc. ) and for each trial found the times ti of these events. After aligning all trials on t1=0 (left) we computed median times Ti for all other events. Then for each trial we re-stretched the firing rate on each interval [ti,ti+1] to align it with [Ti,Ti+1] (right). After such re-stretching, all events are aligned and the trials corresponding to one condition can be averaged. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the methodology of aligning and analyzing trials in experiments related to olfactory stimuli and neural firing rates. It does not address the concept of memory recall or provide any information that supports or contradicts the idea that memory can be perfectly recalled with the right stimulus.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Nitrogen content of litter fall in increasingly nitrogen-poor forest floors was correspondingly lower. Nitrogen content of litter fall on N rich forest floors and N fertilized forest floors was proportionately higher. Nitrogen withdrawal in leaves at senescence was inversely correlated with grams N mineralized per square metre per year in forest floors. Fertilization did not influence microbial processes in the field, though lab studies indicated a negative influence of NH4, P, and K on microbial respiration. Glucose added in the laboratory and field markedly increased forest-floor microbial respiration. In vitro glucose-induced increases in respiration were not influenced by addition of ammonium nitrate and were significantly depressed by addition of P and K. In the field, fertilization had no effect on either glucose-induced respiration or microbial biomass. RésuméLes auteurs ont étudié l'effet des propriétés chimiques de la couverture morte sur le taux de décomposition de la matière organique et le cycle des éléments dans plusieurs forêts de conifères et d'espèces décidues de la taïga de l'intérieur de l'Alaska, sur une période de 5 ans. Au moyen des techniques de sac à litière et de respirométrie, ils ont mesuré les pertes de poids et la consommation d'oxygène d'échantillons de la couverture morte, soit sans traitement ou avec addition d'engrais N, P et K, et de glucose comme source de carbone. En utilisant des échantillons de couverture morte possédant un éventail de rapports C/N, y compris des échantillons carences en azote, on a pu mesurer l'influence de la nature du substrat sur la croissance de semis et leur teneur en éléments et en tannins. [SEP] Justify:

Output: The text corroborates the claim by discussing the decomposition of organic matter and nutrient cycling in forest ecosystems, particularly emphasizing the role of litter fall and the effects of nitrogen content on microbial respiration and organic matter decomposition. It mentions the study of chemical properties of litter and their influence on decomposition rates, which supports the claim about fungi's role in nutrient cycling.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Statistical differences between the extinction thresholds found for each fragmentation scenario were again established based on bootstrapped 95% CIs. ResultsOf the 16 forest understory species modeled, robust models meeting all three goodness of fit criteria were obtained for nine, comprising five herbs (Monotropa hypopitys, Hepatica nobilis, Teucrium scorodonia, Poa nemoralis and Deschampsia flexuosa), and four ferns (Polystichum aculeatum, Dryopteris filix-mas, Athyrium filix-femina and Blechnum spicant) (Table 2). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. Goodness-of-fit statistics for the forest cover and fragmentation logistic models generated for ten herbs and six ferns inhabiting forest understories in Peninsular Spain. Sensitivity to forest amount per se and fragmentation per se As expected for forest understory plants, all species responded negatively to forest cover contraction in absence of fragmentation (a negative Ωj,cover with 95% CIs not including zero) (Fig 2A). In contrast, for the scenario of increased fragmentation, only two species showed negative Ωj,fragm values with 95% CIs not overlapping zero (Fig 2B). These species were thus significantly negatively affected by fragmentation and comprised one herb (M. hypopitys) and one fern (P. aculeatum), although the latter had a weaker response as indicated by the close proximity of its upper 95% CI to zero. Additionally, five species, the herbs P. nemoralis and D. flexuosa, and three ferns (D. filix-mas, A. filix-femina, and B. spicant) responded positively and, hence, are more likely to be found in more fragmented cells. Again, D. flexuosa had a weaker response as indicated by the close proximity of its lower 95% CI to zero. The occurrence of two herbs was not significantly affected by fragmentation (H. nobilis, and T. scorodonia). As groups, herbs and ferns were equally affected by forest contraction (Fig 2C). Ferns were, on average, favored by fragmentation, and herbs did not show sensitivity to fragmentation different from zero (Fig 2D). [SEP] Justify:

Output: The text contrasts the claim by suggesting that not all species are negatively affected by habitat fragmentation. It specifically mentions that while some species exhibited a significant negative response to fragmentation, others responded positively or were not significantly affected. For instance, it notes that five species responded positively to fragmentation and that certain herbs did not show sensitivity to fragmentation, indicating a more complex relationship between habitat fragmentation and species survival than simply being one of the leading causes of extinction.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We made sure that time warping did not introduce any artifacts by considering an alternative procedure, where short ( ± 450 ms) time intervals around each ti were cut out of each trial and concatenated together; this procedure is similar to the pooling of neural data performed in the original studies (Feierstein et al. , 2006; Kepecs et al. , 2008). The dPCA analysis revealed qualitatively similar components (data not shown). Cross-validation to select regularization parameter Request a detailed protocol As noted above, we renormalized the regularization parameter μ=(λ∥𝐗∥)2, and then used cross-validation to find the optimal value of λ for each dataset. To separate the data into training and testing sets, we held out one random trial for each neuron in each condition as a set of SQ test 'pseudo-trials' 𝐗test (as the neurons were not recorded simultaneously, we do not have recordings of all N neurons in any actual trial). Remaining trials were averaged to form a training set of PSTHs 𝐗~train and an estimate of the noise covariance matrix 𝐂~train. Note that 𝐗test and 𝐗~train have the same dimensions. We then performed dPCA on 𝐗~train for various values of λ between 10-7 and 10-3 (on a logarithmic grid). For each λ, we selected ten components in each marginalization (i. e. 40 components in total) to obtain 𝐅ϕ(λ) and 𝐃ϕ(λ), and computed the normalized reconstruction error LCV(λ) on the test set (see below). We repeated this procedure ten times for different train-test splittings and averaged the resulting functions LCV(λ). In all cases the average function L¯CV(λ) had a clear minimum (Figure 11) that we selected as the optimal λ. The values of λ selected for each dataset were 2. 6⋅10-6 5. 8⋅10-6 5. 8⋅10-6 5. 8⋅10-6. [SEP] Justify:

Output: The text does not address the claim directly or indirectly. It focuses on methodological details regarding the analysis of neural data and parameter selection in experiments but does not discuss memories, recall, or any stimuli related to memory retrieval.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: La biomasse microbienne (bactéries et champignons) fut mesurée pour un éventail de types forestiers, de même que le pH, la saturation en bases, l'ampleur des réserves de N et P, et la minéralisation de la matière organique. Pour des conditions identiques de température et d'humidité, les taux moyens de respiration des substrats L, F et H de couverture morte sous couvert résineux étaient respectivement 1,8, 2,8 et 2,0 fois moindre que les taux moyens de respiration des horizons correspondants sous couvert feuillu. Le taux de respiration des horizons L et F de forêt de bouleau était 11,5 fois plus élevé que celui des horizons correspondants de forêt d'épinette noire. Les pertes de poids des horizons L, F et H de forêt de bouleau étaient 6, 3 et 2 fois plus élevées que celles des substrats correspondants de forêt d'épinette noire. La décomposition des substrats s'est avérée influencée davantage par les caractères intrinsèques de ces derniers que par les conditions climatiques de terrain. La biomasse fongique était corrélce à la quantité de matière organique en considérant tous les sites (n = 15, r = 0,62), mais les corrélations étaient meilleures pour les sites décidus (n = 9, r = 0,89), et résineux (n = 6, r = 0,82) pris séparément. [SEP] Justify:

Output: The text corroborates the claim by discussing the correlation between fungal biomass and the amount of organic matter. It indicates that the decomposition of substrates is significantly influenced by their intrinsic characteristics and points out the role of fungal biomass in the overall decomposition process of organic materials across different forest types.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Apart from supporting the fragmentation threshold hypothesis, these results again suggest a stronger sensitivity to increased forest loss and fragmentation for M. hypopitys, as this species first reached the 0. 5 probability at a higher forest cover in each fragmentation scenario and also showed significantly different occurrence probabilities between both scenarios at all forest covers except for the highest (PFC = 85%, see Fig 3A). In contrast, for P. aculeatum these significant differences only occurred at intermediate PFC values (see Fig 3F). On the other hand and consistent with the sensitivity analysis, there were two herbs that showed no significantly different extinction thresholds between both fragmentation scenarios, H. nobilis and T. scorodonia (see Fig 3B and 3C), and five species that exhibited the opposite response to that predicted by the fragmentation hypothesis (i. e. a significantly higher extinction threshold under no fragmentation): the herbs D. flexuosa and P. nemoralis, and the ferns A. filix-femina, D. filix-mas and B. spicant (Fig 3D,3E and 3G–3I). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 3. Occurrence probability of the five herbs and four ferns under the two scenarios of forest fragmentation. The nine predictive values of forest cover (PFC) range from 5% to 85%. White circles represent occurrence probabilities in the maximum fragmentation scenario, while black circles represent occurrence probabilities in the minimum fragmentation scenario. Confidence intervals at 95% are given. Note that PFC decreases to the right. together, these results suggest that support for the fragmentation threshold hypothesis is rare among forest understory plant species, and also that some of these species may even react positively to increased fragmentation. Moreover, herbs do not show negative responses to fragmentation, i. e. , our expectation based on lower dispersal abilities of forest herbs was not supported. DiscussionThe array of responses to fragmentation showed by the modeled species underscore the complexity that interacting ecological processes associated with fragmentation may have on specific species [13]. Lack of support for our expectation based on the low dispersal capability of forest herbs could indicate that other factors contribute to species’ responses to fragmentation [28]. We suggest that variation in shade tolerance across species, and hence, in their respective possibilities to survive in more open, fragmented habitats (e. g. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that some species, particularly certain forest herbs, may not show negative responses to fragmentation, suggesting that not all species are negatively affected by habitat fragmentation. It highlights that some species may react positively to increased fragmentation and that the anticipated negative impact on forest herbs due to their lower dispersal abilities was not supported. This evidence raises questions about the assertion that habitat fragmentation universally leads to species extinction.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We used 100 iterations of stratified leave-group-out cross-validation, where on each iteration we held out one trial for each neuron in each condition as a set of SQ test 'pseudo-trials' 𝐗test and averaged over remaining trials to form a training set 𝐗~train (see above). After running dPCA on 𝐗~train, we used decoding axes of the first three stimulus/decision/interaction dPCs as a linear classifier to decode stimulus/decision/condition respectively. Consider e. g. the first stimulus dPC: first, for each stimulus, we computed the mean value of this dPC separately for every time-point. Then we projected each test trial on the corresponding decoding axis and classified it at each time-point according to the closest class mean. The proportion of test trials (out of SQ) classified correctly resulted in a time-dependent classification accuracy, which we averaged over 100 cross-validation iterations. Note that this is a stratified procedure: even though in reality some conditions have many fewer trials than others, here we classify exactly the same number of 'pseudo-trials' per condition. At the same time, as the coordinates of individual data points in each pseudo-trial are pooled from different sessions, the influence of noise correlations on the classification accuracies is neglected, similar to Meyers et al. (2012). We then used 100 shuffles to compute the distribution of classification accuracies expected by chance. On each iteration and for each neuron, we shuffled all available trials between conditions, respecting the number of trials per condition (i. e. all ∑sdKnsd trials were shuffled and then randomly assigned to the conditions such that all values Knsd stayed the same). Then exactly the same classification procedure as above (with 100 cross-validation iterations) was applied to the shuffled dataset to find mean classification accuracy for the first stimulus, decision, and interaction components. All 100 shuffling iterations resulted in a set of 100 time-dependent accuracies expected by chance. The time periods when actual classification accuracy exceeded all 100 shuffled decoding accuracies in at least ten consecutive time bins are marked by black lines on Figures 3–6. Components without any periods of significant classification are not shown. 12 for classification accuracies in each dataset. [SEP] Justify:

Output: The text discusses a complex procedure involving data classification, cross-validation, and decoding accuracies, but does not address the claim about perfect recall of memory with the right stimulus. It focuses on methodologies in a scientific study rather than providing any insight into memory recall or the effectiveness of stimuli in that context. Therefore, this text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: L'étude révèle l'existence de corrélations étroites entre le taux de décomposition de la matière organique et la biomasse fongique (n = 13, r = 0,86), et entre la biomasse fongique et la quantité de N et P minéralisé (n = 14, r = 0,95; n = 11, r = 0,94, respectivement). Les semis cultivés sur substrat carencé en éléments ont produit plus de tannins que les témoins, et les semis cultivés sur substrat classé par ordre croissant du rapport C/N ont formé moins de matière sèche, avec un contenu moindre en azote et avaient proportionnellement une masse racinaire plus élevée. Parallèlement, la teneur en azote de la litière s'atténuait avec l'augmentation du rapport C/N des substrats. La résorption de l'azote des feuilles à la sénescence était corrélée inversement avec la quantité de N minéralisé par mètre carré et par an. La fertilisation n'a pas eu d'influence sur les processus microbiens au champ, malgré que les études en laboratoire indiquaient une influence négative de NH4, P et K sur la respiration microbienne. L'addition de glucose, tant au champ qu'en laboratoire, a augmenté fortement la respiration. L'augmentation de respiration induite par le glucose invitro ne fut pas influencée par l'addition de nitrate d'ammonium, mais elle fut réduite significativement par l'addition de Pet K. La fertilisation au champ fut sans effet sur la respiration induite par le glucose, non plus que sur la biomasse microbienne. [Traduit par le journal]Get full access to this 1983. Nutrient cycling in relation to decomposition and organic-matter quality in taiga ecosystems. of Forest Research. [SEP] Justify:

Output: The text supports the claim by discussing correlations between fungal biomass and organic matter decomposition rate, indicating that fungi play a significant role in nutrient cycling through their effects on nitrogen and phosphorus mineralization. It mentions specific statistical relationships (e.g., r = 0.86 between organic matter decomposition and fungal biomass), which further corroborate the idea that fungi are integral to the cycling of nutrients in ecosystems.


Input: Task: contrast the claim using the given text. Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: seeds dispersed by ants [28], and absence of clonal growth or seed bank [45]) make this species a candidate for negative impacts of fragmentation, it has been observed that increased shade from trees may impair its flowering and cause considerable declines in its populations, and also that the species is able to survive in open wooded meadows [46]. This may explain why this species shows null responses to fragmentation (Fig 3B). Teucrium scorodonia and Deschampsia flexuosa have also been described in hedges, heaths and grasslands—that probably owe their origin to the clearance of original woodland—in Britain [47, 48] and the [49, 50]. Also, germination of both species is indeed less successful in the shade/closed canopy compared with sunny conditions [48, 51]. Further, Poa nemoralis, one of the species clearly favored by forest fragmentation (Fig 3E), has a germination rate of up to 60% in the sun in contrast to 7% in the shade [52], and when this species occurs in the densest canopy forest cover is in the form of scattered and sterile individuals [51]. Also, this species is effectively dispersed in animal fur or in deer dung [53, 54], which would facilitate the colonization of forest fragments [55]. All of these characteristics suggest that, although able to survive in dense canopy forests, these herbs grow and reproduce better in forests with sparse canopy, forest gaps or even forest edges. Together with the typical problems of empirical studies, e. g. to use a general definition of habitat for all species (forest), our analysis entails several limitations that might make unclear the response of the species in some cases. Our database comprises species presence/absence in 10 km x 10 km grid cells, which can be too coarse a resolution to detect some fragmentation effects. Thus, we lack information about the processes acting at the landscape level, namely demographic processes such as source-sink dynamics that are not captured by presence/absence data. However, using species densities, Montoya et al. [23] only confirmed the extinction threshold hypothesis for one out of eight tree species, which may suggest that, in general, forest species may show a weak response to fragmentation due to their own idiosyncrasy (e. g. long-lived perennials). Additionally, from the wealth of existing metrics quantifying habitat configuration and landscape matrix characteristics (e. g. [SEP] Justify:

Output: The text contrasts the claim by discussing specific species responses to habitat fragmentation, indicating that some species show 'null responses to fragmentation,' suggesting they are not significantly affected by it. It also mentions that certain species, although able to survive in dense canopy forests, grow and reproduce better in conditions of sparse canopy and forest edges, which implies that habitat fragmentation doesn't necessarily lead to extinction for all species.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Carlo computations took ∼8 hr for each of the larger datasets on a 6 core 3. 2 i7-3930K processor. Figure 12 Download asset Open asset Cross-validated time-dependent classification accuracies of linear classifiers (black lines) given by the first three stimulus/decision/interaction dPCs (columns) in each dataset (rows). Shaded gray regions show distribution of classification accuracies expected by chance as estimated by 100 iterations of shuffling procedure. Implementation of classical approaches (Figure 1) Request a detailed protocol The two-way ANOVA shown in Figure 1c–e was performed as follows. The two factors were stimulus (with six levels) and decision (with two levels), the interaction term was included, and a separate ANOVA was run for the firing rate of each neuron at each time point. Significance level was set at α=0. 05. Effect size was defined as partial omega squared with a sign given by the sign of the correlation coefficient between firing rate and the corresponding parameter. It can take values between -1 and 1, with 0 meaning no effect. For one-way ANOVA with a single two-level factor (which is a t-test), it would reduce to the signed R2 between firing rate and factor level. 1f–g, we ran linear regressions for the firing rate of each neuron at t=0. 25 s and t=3. 75 s, taking F1 stimulus value in Hz as one predictor, decision as another one, and including an interaction effect. Predictors were standardized (so regression coefficients in Figure 1g are standardized coefficients). The components shown in Figure 1f were constructed following the 'targeted dimensionality reduction' method presented in Mante et al. (2013) (see below for more details). To compute the proportion of explained PSTH variance, we arranged the two components (obtained by either method) into a matrix 𝐙 of 2×SQT size. Both the PSTH data matrix 𝐗~ and 𝐙 were centered by subtracting row means. Linear regression was used to find reconstruction weights 𝐁=𝐗~𝐙⊤(𝐙𝐙⊤)-1 minimizing reconstruction error ∥𝐗~-𝐁𝐙∥2. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses computational methods and statistical analyses related to neural firing rates and does not address the concept of memory recall or the efficacy of stimuli in improving memory recall. There is no mention of memory or the capability of recall in the provided text.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Influence of the N and P status of plant material and of added N and P on the mineralization of C from 14C-labelled ryegrass in soil Go to 231. of to 232. Interactions between 14C-labelled atrazine and the soil microbial biomass in relation to herbicide degradation Go to 233. in the Go to 234. Decomposition of birch leaf litters with varying C-to-N ratios Go to 235. Changes in nutrient availability and forest floor characteristics in relation to stand age and forest composition in the southern part of the boreal forest of northwestern to 236. An assessment of the possible impact of expansion of native woodland cover on the chemistry of Scottish freshwaters Go to 237. Soil carbon cycling in a Japanese cedar (Cryptomeria japonica) plantation Go to 238. Development patterns in young conifer‐hardwood forests of interior to 239. Direct and of on in Go to 240. Nutrient cycling strategies Go to 241. Nitrogen cycling and dynamic analysis of man made larch forest ecosystem Go to 242. Nutrient cycling strategies Go to 243. Nitrogen cycling and dynamic analysis of man made larch forest ecosystem Go to 244. Removal of nitrogen during needle senescence in Scots pine (Pinus sylvestris L. ) Go to 245. Decomposition of tree leaf litters grown under elevated CO2: Effect of litter quality Go to 246. Influence of herbicide applications on the decomposition, microbial biomass, and microbial activity of pasture shoot and root litter Go to 247. Microsite and regional variation in the potential decay rate of Sphagnum magellanicum in south Swedish raised bogs Go to 248. The influence of different forest organic matter on the growth of one-year old planted Norway spruce seedlings in a greenhouse experiment Go to 249. Decomposition and nutrient dynamics of litter in long‐term optimum nutrition experiments. 11. Nutrient concentrations in decomposing Picea abies needle litter Go to 250. Changes in the forest floor of Canadian southern boreal forest after disturbance Go to 251. Phosphorus and nitrogen dynamics during field incubations in forest and fallow subarctic soils Go to 252. Forest responses to CO2 enrichment and climate warming Go to 253. Moose herbivory, browse quality, and nutrient cycling in an Alaskan treeline community Go to 254. [SEP] Justify:

Output: The text discusses various aspects of nutrient cycling, particularly in relation to the decomposition of organic materials. It addresses topics like the decomposition of birch leaf litters and other forest floor characteristics, which aligns with the claim that fungi contribute to nutrient cycling through the decomposition of organic matter. The focus on soil microbial biomass and interactions also suggests the role of microorganisms, including fungi, in these processes, thereby supporting the claim.


Input: Task: pass the claim using the given text. Claim: The human brain contains approximately 86 billion neurons, which communicate through electrical and chemical signals. [SEP] Text: (c) Quantification of CB+, CR+, SST+, and PV+ cells among the TUJ-1+ cells. Upper, H9-01 LHX6 OE; below, ihtc-03 LHX6 OE group. (d) SST and PV expression in the ihtc-03 LHX6 OE group. (e) Neurons expressing SST and PV interneurons showed a characteristic morphological structure with more than five secondary branches. Figure 3 with 1 supplement Download asset Open asset Generation of SST and PV subtypes by overexpressing LHX6 and ventral patterning. (a) Schematic showing the differentiation of ventral cells from transgenic hPSC lines after treatment with SAG. V-CON, ventral control group (−dox); V-LHX6 OE, ventral LHX6 OE group (+dox). (b) The mRNA expression levels of ventral transcriptional markers in three transgenic hPSC lines and in each control at 17 days post-differentiation; n ≥ 3 biological replicates. (c) The proportions of LHX6+ cells in the control and LHX6 OE groups. The NKX2. 1, -1 groups all displayed high expression of LHX6 in both the H9-01 and ihtc-03 cell lines with or without dox. Scale bar, 50 μm. (d–e) Quantification of NKX2. 1+ cells (d) and GINs (e). (f) Percentage of GINs in the ventral ihtc-03 LHX6 OE group. At least 1500 cells were counted from random selected fields in each cell line, n ≥ 3 for each cell line. (g) Immunostaining of GIN subtypes in the ventral ihtc-03 control (V-CON) and LHX6 OE groups. (h) Representative tracing images of three different types PV+ neurons (type I, 130/369; type II, 137/369; type III multipolar cells, 102/369). Figure 3—figure supplement 1 Download asset Open asset GIN progenitors identity and subtype distribution. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses experimental results related to different cell types and markers without providing any information about the total number of neurons in the human brain or their communication mechanisms. It focuses on specific subtypes and methodologies rather than corroborating or contrasting the claim regarding neuron count and function.