Input: Task: pass the claim using the given text. Claim: Ginkgo biloba is considered a living fossil, having existed for more than 200 million years. [SEP] Text: and Biochemistry 37:1083-1091. 2003. Community ecology in a typical near natural urban forest. of Ecology 22:162-168. 2009. Changes of main secondary metabolites in leaves of Ginkgo biloba in response to ozone fumigation. Journal of 21:199-203. 2016. Effects of climate warming on phenological characteristics of urban forest in Shenyang City, China. Science 26:1-9 IPCC. 2013. Climate change, 2013: the physical science basis. Cambridge: Press. Julkunen-Tiitto R. 1985. Phenolic constituents in the leaves of northern willows: methods for the analysis of certain phenolics. Agricultural and 33:213-217. 2003. Decomposition of secondary compounds from needle litter of Scots pine grown under elevated CO2 and O3. Biology 9:295-304 Lavola A, Julkunen-Tiitto R, Paakkonen E. 1994. Does ozone stress change the primary or secondary metabolites of birch (Betula pendula Roth. )? 126:637-642. 2011. Effects of elevated carbon dioxide and/or ozone on endogenous plant hormones in the leaves of Ginkgo biloba. Plantarum 33:129-136. 2001. Consequences of elevated carbon dioxide and ozone for foliar chemical composition and dynamics in trembling aspen (Populus tremuloides) and paper birch (Betula papyrifera) 115:395-404. 2004. Changes in solute content of different tomato genotypes under salt stress. Sinica 30:99-104. 2009. The influence of chemistry, production and community composition on leaf litter decomposition under elevated atmospheric CO2 and tropospheric O3 in a northern hardwood ecosystem. Ecosystems 12:401-416. 1988. An improved acetyl bromide procedure for determining lignin in woods and wood pulps. and Technology 22:271-280. 2009. Effects of elevated O3 and/or elevated CO2 on lipid peroxidation and antioxidant systems in Ginkgo biloba leaves. Bulletin of Toxicology 83:92-96. 2012. Effects of ozone on membrane lipid peroxidation and protective enzyme activities of Thevetia peruviana and Mangifera leaves. Ecology & 21:1235-1240 Luo Z, Zhang L. 2010. [SEP] Justify:

Output: The text does not address the claim regarding Ginkgo biloba being a living fossil or its historical existence spanning over 200 million years. Instead, it discusses various studies related to the effects of environmental factors on Ginkgo biloba and does not provide any information supporting or contrasting the claim.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In each subplot there are ten lines corresponding to ten conditions (see legend). Six out of these ten conditions were rewarded and are shown with thick lines; note that the pure left (red) and the pure right (blue) odors did not have error trials. Inset shows mean rate of the second interaction component during the anticipation period. (c) Cumulative variance explained by PCA and dPCA components. (d) Explained variance of the individual demixed principal components. Pie chart shows how the total signal variance is split between parameters. (e) Upper-right triangle shows dot products between all pairs of the first 15 demixed principal axes, bottom-left triangle shows correlations between all pairs of the first 15 demixed principal components. We analyzed the activity of 214 OFC neurons from three rats recorded in 8 conditions, corresponding to four odor mixtures, each paired with two decisions (left and right). During the presentation of pure odors (100% right and 100% left) rats made essentially no mistakes, and so we excluded these data from the dPCA computations (which require that all parameter combinations are present, see Discussion). Nevertheless, we displayed these additional two conditions in Figure 6. The dPCA components shown in Figure 6b are similar to those presented in Figure 5b. Here again, some of the interaction components (especially the second one, #5) show strong tuning already during the anticipation period, i. e. before the actual reward delivery. The inset in Figure 6b shows the mean value of the component #5 during the anticipation period, separating correct (green) and incorrect (red) trials for each stimulus. The characteristic U-shape for the error trials and the inverted U-shape for the correct trials agrees well with the predicted value of the rat’s uncertainty in each condition (Kepecs et al. , 2008). Accordingly, this component can be interpreted as corresponding to the rat’s uncertainty or confidence about its own choice, confirming the results of Kepecs et al. (2008). In summary, both the main features of this dataset, as well as some of the subtleties, are picked up and reproduced by dPCA. Universal features of the PFC population activity One of the key advantages of applying dPCA to these four datasets is that we can now compare them far more easily than was previously possible. [SEP] Justify:

Output: The text does not address or provide any information related to the claim about memory recall. It discusses experimental conditions, neuronal activity, and analysis methods without touching upon the concept of memory or stimulus effect on recollection.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: On average, 19 kg N ha−1, 3 kg P ha−1, 30 kg K ha−1, 4 kg Ca ha−1 and 4 kg Mg ha−1 exported via annual harvest (cocoa pod husk and beans) represented 5%, 7%, 29%, 2%, and 4%, respectively, of the annual nutrient stocks recycled in total litterfall (Table 4). Table 4 Carbon and nutrient stocks of the annual litterfall components, cocoa bean and pod husk of cocoa agroforests under conventional and organic management in Suhum Municipality, of GhanaFull size tableDecomposition of cocoa leafWithin the first 2 months, 50% of the initial litter DM disappeared under organic and conventional management (Fig. 3a). After 1 month of cocoa leaves placement, P and Ca release under conventional management were 20% and 21%, respectively, higher (p < 0. 05) than under organic management (Fig. 3c and e). Similarly, Mg release at months one and two after cocoa leaves placement were 24% and 10%, respectively, lower (p < 0. 05) under organic management than under conventional management (Fig. 3f). At the end of the 12 months study period, on average 15% of the initial litter remained in the litterbags. Potassium was rapidly released from litter leading to the disappearance of 98% of the initial content. Although DM disappearance and N release rates were 35% greater under organic management compared with conventional management, these differences were not statistically significant (Table 5). Phosphorus, K, and Ca release rates were 13%, 33%, and 41%, respectively, higher in conventional farms than organic farms, but these differences were not significant. For P, K, Ca, and Mg 93–100% of nutrients belong to the labile fraction of the cocoa leaves. Among the nutrient release rates, it was only Mg that under conventional management was significantly (51%) greater than under organic management. Fig. 3Decay and nutrient release patterns of cocoa leaves during 12 months in cocoa agroforests under conventional (Con) and organic (Org) management in Suhum Municipality, of Ghana. Error bars indicate +/− one standard error of the mean. * and ** above the error bars indicate significant differences at p = 0. 05 and 0. [SEP] Justify:

Output: The text corroborates the claim by discussing the decomposition processes of cocoa leaves, where it mentions that significant proportions of nutrients are recycled via the decomposition of litter (specifically cocoa leaves). It illustrates the dynamics of nutrient release, such as nitrogen, phosphorus, potassium, calcium, and magnesium, during the decomposition and highlights the efficiency of organic and conventional management practices in nutrient cycling within cocoa agroforests.


Input: Task: pass the claim using the given text. Claim: Ginkgo biloba is considered a living fossil, having existed for more than 200 million years. [SEP] Text: Головко Е. А.  Головко Н. В. Головкова Юлия Сергеевна Головчанов С. С. Головчанов С. С. Голубина Наталья Васильевна Голубовская С. П. Гольц В. А. Гонтарь О. Б. Гончарова А. А. Гончарова Н. В. Гончарова Н. Д. Гордеева М. А. Горелов О. И. Горелов С. В. Горкуша О. А. Горохов Андрей Анатольевич Горшенин В. П. Горшенин В. П. Горюк Н. В. Горюнова О. В. Гофман О. О. Грачева П. С. Гребень А. И. Гречкина Л. И. Гриб Г. В. Гриб Н. Н. Григорьев И. В. Григорьева О. И. Гринько О. [SEP] Justify:

Output: The text is irrelevant to the claim as it contains a list of names and does not provide any information regarding Ginkgo biloba or its status as a living fossil. There is no mention or context provided 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: This comparison allows us to highlight several general features of the population activity in prefrontal areas. First, most of the variance of the neural activity is always captured by the condition-independent components that together amount to 65–90% of the signal variance (see pie charts in Figures 3–6d; see Materials and methods for definition of 'signal variance'). These components capture the temporal modulations of the neural activity throughout the trial, irrespective of the task condition. Their striking dominance in the data may come as a surprise, as such condition-independent components are usually not analyzed or shown (cf. Figure 1e,h), even though condition-independent firing has been described even in sensory areas (Sornborger et al. , 2005). These components are likely explained in part by an overall firing rate increase during certain task periods (e. g. during stimulus presentation). More speculatively, they could also be influenced by residual sensory or motor variables that vary rhythmically with the task, but are not controlled or monitored (Renart and Machens, 2014). The attentional or motivational state of animals, for instance, often correlates with breathing (Huijbers et al. , 2014), pupil dilation (Eldar et al. , 2013), body movements (Gouvêa et al. , 2014), etc. Second, even though dPCA, unlike PCA, does not enforce orthogonality between encoding axes corresponding to different task parameters, most of them turned out to be close to orthogonal to each other (Figures 3–6e, upper triangle), as has been observed before (Brendel et al. , 2011; Rishel et al. , 2013; Raposo et al. , 2014). Nevertheless, many pairs were significantly non-orthogonal, meaning that neurons expressing one of the components tended to also express the other one. Throughout the four datasets, we identified 277 pairs of axes (among the first 15 axes) corresponding to different parameters. Of these, 38, i. e. 14%, were significantly non-orthogonal with p<0. 001 (8 out of 53 if we do not take time axes into account). Third, all dPCA components in each of the datasets are distributed across the whole neural population (as opposed to being exhibited only by a subset of cells). [SEP] Justify:

Output: The text does not relate to the claim regarding memory recall. It discusses neural activity and components in prefrontal areas without addressing memory recall or the effects of stimuli on memory. Thus, it is classified as irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Although pruned litter from shade and cocoa trees influences the nutrient cycling process in cocoa agroforests, it could not be quantified with litterboxes. Management did not significantly alter cocoa leaf decompositionInitial rapid decomposition, as observed under both managements, was likely caused by leaching and break down of soluble compounds like sugar, starch, and amino acids (Wieder and Lang 1982). The disappearance of 50% of the initial material in the first 2 months is common in tropical agroforestry systems (Yao et al. 2021). Particularly cocoa agroforests with high litter diversity on the soil surface host a diverse community of soil and litter macro- and microfauna (da Silva Moço et al. 2009) accelerating litter fragmentation. Later stages of decomposition were more gradual because of accumulation of recalcitrant compounds like cellulose, lignin, and tannins (Wieder and Lang 1982). Our average decomposition rate of 0. 40 g month−1 falls within the range of 0. 30–0. 58 g month−1 reported for cocoa leaves in Nigeria (Afolabi and Muoghalu 2018) and Cote d´Ivoire (Yao et al. 2021). However, Asigbaase et al. (2020) reported an average decomposition rate of 1. 69 g month−1 in cocoa agroforests of the same municipality as ours, but it was 300% higher than ours. This disparity could be due to the above authors mixing cocoa leaves with shade tree leaves at a 1:1 rate. In our study decomposition and nutrient release rates were determined for cocoa leaves alone without including leaves from surrounding shade trees. A mixture of leaves from different species alters litter chemistry, which could be synergistic or antagonistic to decomposition processes and nutrient release (Cuchietti et al. 2014). Because of the high shade tree variability even between farms of the same management, we only considered cocoa leaves for the decomposition study. Hence, our decomposition results were not a complete reflection of litter decomposition in cocoa agroforests. In addition, the different methods that exist to study litter decomposition can lead to discrepancies when comparing studies. For example, in litterbag experiments mesh sizes > 2 mm have been reported to increase litter decomposition due to the increased activity of macro fauna which enhances litter fragmentation exposing them to higher microbial activity. [SEP] Justify:

Output: The text corroborates the claim by discussing the decomposition processes in cocoa agroforests, where the interaction of diverse macro- and microfauna contributes to the fragmentation and breakdown of organic matter, which is essential for nutrient cycling. The presence of decomposing organic litter, including cocoa leaves, suggests the role of various organisms in enhancing the decomposition process, thereby contributing to the overall nutrient cycling in these ecosystems.


Input: Task: pass the claim using the given text. Claim: Some plants communicate with each other through chemical signals released by their roots or leaves. [SEP] Text: Staining of mitochondria and F-actin for fixed-cell based microscopy analysis For fixed-cell based analysis, single-cell suspension of hNSCs was incubated with 100 nM Mito-Tracker Deep-Red FM (MT; Invitrogen, cat. #M22426) diluted in hNSC-medium. After 30min of incubation, cells were centrifuged e resuspended in fresh prewarmed hNSC-medium at least two times, left overnight in the hNSC-medium and centrifuged. The cell pellet was washed once to clear the excess dye completely and seeded on cultrex-coated glass. To reduce the breakdown of TNT and maintain the fluorescence of MitoTracker Deep-Red, cells were fixed directly in the medium by adding formaldehyde to a final concentration of 3. 7% v/v for 15min at 37°C. Afterward, samples were carefully washed with PBS, permeabilized by 0. 3% Triton X-100 in PBS for 15min, blocked with 3% BSA in PBS, and incubated with AlexaFluor488- Phalloidin (1:500; Thermo Fisher, cat. #A12379) for 60min at room temperature in order to stain F-actin. After careful washing, the samples were mounted in Vectashield antifade mounting medium (cat. #H-1000-10). Live-cells time lapse fluorescence microscopy For live-cell imaging, single cells or neurospheres were seeded on confocal dishes (Greiner Bio-One, cat. #627870), and images were acquired by Biostation IM-Q at 37°C, humidity level of 95% and 5% CO2. To correctly visualize the mitochondria’ dynamics, images were acquired every 5–7 seconds with a low-intensity light source to reduce phototoxicity and photobleaching of the dye. Timelapse images were analyzed using software and FiJi software. Confocal microscopy and Tau-STED microscopy of hNSCs in 2D-culture and sectioned neurospheres Routine confocal microscopy was performed using a 5 microscope. All acquisitions were performed by serial acquisition mode between frames. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses procedures related to fixed-cell microscopy analysis and live-cell imaging without mentioning plants or their communication methods. Therefore, it does not support or contrast the claim.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: For each component and each neuron, the corresponding encoder weight shows how much this particular component is exhibited by this particular neuron. For each component, the distribution of weights is strongly unimodal, centred at zero (Figure 7a), and rather symmetric (although it is skewed to one side for some components). In other words, there are no distinct sub-populations of neurons predominantly expressing a particular component; rather, each individual neuron can be visualized as a random linear combination of these components. We confirmed this observation by applying a recently developed clustering algorithm (Rodriguez and Laio, 2014) to the population of neurons in the 15-dimensional space of dPC weights. In all cases, the algorithm found only one cluster (Figure 7b). An alternative clustering analysis with Gaussian mixture models yielded similar results (data not shown). This absence of any detectable clusters of neurons has been noted before (Machens et al. , 2010) and was recently observed in other datasets as well (Raposo et al. , 2014). Figure 7 Download asset Open asset Encoder weights for the leading dPCA components across the neural population. (a) Distributions of encoder weights for the 15 leading dPCA components across the neural population, in each of the four datasets. Each subplot shows 15 probability density curves, one curve per component (bin width 0. 005). The distribution corresponding to the first component is highlighted in red. (b) Clustering of neurons by density peaks (Rodriguez and Laio, 2014). For each dataset we took the first 15 dPCA components, and then ran the clustering algorithm in the 15-dimensional space of encoding weights. The clustering algorithm works in two steps: first, it computes a local density for each point (i. e. , for each neuron), using a Gaussian kernel with σ2=0. 01. Second, for each point it finds the minimal distance to a point with higher local density (if there is no such point, then the distance to the furthest point is taken). Each subplot shows local density on the horizontal axis plotted against distance to the next point with higher density on the vertical axis; each dot corresponds to one of the N neurons. [SEP] Justify:

Output: The text discusses the neural encoding of components and the clustering of neurons but does not address memory recall or any claims related to the perfection of memory retrieval with stimuli. Thus, the 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: Recycling cocoa pod husk is important for C and nutrient retentionThe recorded cocoa yields of 559 kg ha−1 yr−1 were higher than the 477 kg ha−1yr−1 reported for the mid zone of Ghana (Abdulai et al. 2020), which has similar climatic conditions as Suhum. The average annual nutrient stocks of 19 kg N ha−1, 3 kg P ha−1, 30 kg K ha−1, 4 kg Ca ha−1 and 4 kg Mg ha−1 in the cocoa fruit (beans and pods) falls within the range of 19–29 kg N ha−1, 4–5 kg P ha−1, 15–51 kg K ha−1 (Hartemink 2005), 3–6 kg Ca ha−1, and 6–10 kg Mg ha−1 (Fontes et al. 2014) for other cocoa agroforests across the world. Farmers could reduce C export by 43% and nutrient export from harvest by 33% for N and 25% for P, if they spread the cocoa pod husks in their farms after harvest. This is even more important for nutrients such as K and Ca, where 81% and 90%, respectively, can be retained after harvest if cocoa pod husk were returned. However, the spreading cocoa pod husk is often avoided to prevent the spread of diseases from disease infested pods (Opoku-Ameyaw et al. 2010). Composting of cocoa pod husk could be an option to control disease pathogens (Opoku-Ameyaw et al. 2010) and return nutrients to the cocoa trees. Cocoa farmers in the study area may therefore benefit from training on composting to be able to use cocoa pod husk for improvement of their internal nutrient cycling. ConclusionsOur findings show that litterfall follows a seasonal pattern with peaks at the onset of the dry season. Seasonality affected litterfall K and N concentrations, but organic versus conventional management had no effect on annual litterfall. We found that management had no significant effect on decomposition and nutrient release rates of N, P, K, and Ca. Only the Mg release rate under conventional management was 51% greater than under organic management, which may be attributed to the chelating capacity of herbicides used. [SEP] Justify:

Output: The text corroborates the claim by discussing how composting cocoa pod husk can control disease pathogens and return nutrients to the cocoa trees, which relates to the overall process of nutrient cycling. It implies that the breakdown of organic matter (cocoa pod husk) improves nutrient retention and supports nutrient cycling within the agroecosystem.


Input: Task: contrast the claim using the given text. Claim: Baobab trees can live for over 1,000 years and store large amounts of water in their trunks. [SEP] Text: The corresponding calibrated ages are 120 ± 25 and 150 ± 25 years. The deepest segment 13x (20 cm) originating from stem II, at a height of 1. 20 m above ground, had a radiocarbon date of 170 ± 22 bp, with a corresponding calibrated age of 250 ± 25 years. 3. 5 Growth rates of the stemsCalibrated ages and positions of segments 11x and 12x, which originate from samples collected from the exterior of stem I, were used to derive the mean radial increase. Thus, stem I had grown by 3. 75 × 10−3 m year−1 over the past ca. 120 years to the height of 1. 34 m, and by 3. 40 × 10−3 m year−1 over the past ca. 150 years to the height of 2. 75 m. Such high values indicate that the very large stem I is still growing very fast. However, as reported elsewhere (Patrut et al. 2010b), the mean radial increase expresses only an apparent growth rate of trunk/stems. Trees grow in three dimensions and their size is accurately expressed by the wood volume of the trunk/stems, which is in proportion to the cross-sectional areas at different heights. Consequently, the area increase and volume increase are much better estimates of the growth rate than the radial increase. If one considers in this respect the dating results and positions of all six samples/segments collected from the cavity and the exterior of stem I, one can state that stem I accelerated its growth over the past ca. 150 years. This result corresponds to our previous research on African baobabs (Patrut et al. 2010b), as well to other research done on giant tree species (Sillett et al. 2010). The calibrated age and position of segment 13x, which originates from the sample collected from the exterior of stem II, shows that this stem had grown by only 0. 80 × 10−3 m year−1 over the past ca. 250 years to the height of 1. 20 m. This low value demonstrates that the smaller and older stem II slowed down its growth considerably and that it could be close to the final stage of the African baobab's life cycle, when trees almost stop growing. 3. [SEP] Justify:

Output: The text contrasts the claim by referencing the calibrated ages of various baobab tree segments which indicate ages of around 120 to 250 years. These findings suggest that the trees discussed may not live for over 1,000 years, contrary to the claim. Additionally, it notes growth rates and conditions that imply some trees are at the final stages of growth, casting doubt on the longevity and large water storage notions.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Cluster centres are characterized by high local density and large distance to the point of even higher density; they should appear as outliers in the upper-right corner of the plot (see Rodriguez and Laio, 2014, for details). In each case, there is only one such outlier (bigger dot), indicating a single cluster. selectivity of neurons in higher cortical areas has been increasingly recognized as a problem for the analysis of neurophysiological recordings, with many different approaches suggested to deal with it (Brody et al. , 2003; Machens et al. , 2010; Machens, 2010; Brendel et al. , 2011; Rigotti et al. , 2013; Pagan and Rust, 2014; Park et al. , 2014; Raposo et al. , 2014; Cunningham and Yu, 2014). The main strength and the main novelty of the method suggested here (dPCA) is that it offers a unified and principled way of analyzing such data. Demixed PCA combines the strengths of existing supervised and unsupervised approaches to neural population data analysis (Table 1, see also the first section of the Results). Supervised methods can characterize population tuning to various parameters of interest but often do not faithfully represent the whole dataset. Unsupervised methods can capture the overall variance but are not informed by task parameters. Our method yields components that capture almost as much variance as PCA does, but are demixed. We view both properties as equally important. On one hand, demixing can greatly simplify visualization and interpretation of neural population data. Indeed, in all cases presented here, all the major aspects of the population activity that had previously been reported are directly visible on the dPCA summary figure. On the other hand, faithful representation of the population activity (i. e. 'capturing variance') avoids that a particular interpretation distorts characteristic features of the data. The latter feature is particularly important for the development of theoretical models, which otherwise may inherit an interpretation bias without being aware of it. Apart from being a useful tool for analyzing any particular dataset, dPCA highlights common features of neural activity when applied to several datasets, allowing to adopt a comparative approach to study population activity. Table 1 Demixed PCA in comparison with existing methods. Columns: 'Signif. ' refers to the method of counting significantly tuned cells, as shown in Figure 1c–e. [SEP] Justify:

Output: The text passes as it does not directly address the claim regarding memory recall. It discusses a method of analyzing neural population data (dPCA) and the characteristics of cluster centers in neurophysiological recordings but does not provide any information supporting or refuting the specific idea that memory can be perfectly recalled given 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: in through your institution issue search term Advance 10. 1038/ismej. 2013. 195 Published: 31 October 2013 10. 1038/ismej. 2013. 195 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issue search term issue search term of northern temperate and boreal forests represent a large terrestrial carbon (C) sink. The fate of this C under elevated atmospheric CO2 and climate change is still uncertain. A fundamental knowledge gap is the extent to which ectomycorrhizal fungi (EMF) and saprotrophic fungi contribute to C cycling in the systems by soil organic matter (SOM) decomposition. In this study, we used a novel approach to generate and compare enzymatically active EMF hyphae-dominated and saprotrophic hyphae-enriched communities under field conditions. Fermentation-humus (FH)-filled mesh bags, surrounded by a sand barrier, effectively trapped EMF hyphae with a community structure comparable to that found in the surrounding FH layer, at both trophic and taxonomic levels. In contrast, over half the sequences from mesh bags with no sand barrier were identified as belonging to saprotrophic fungi. The EMF hyphae-dominated systems exhibited levels of hydrolytic and oxidative enzyme activities that were comparable to or higher than saprotroph-enriched systems. The enzymes assayed included those associated with both labile and recalcitrant SOM degradation. Our study shows that EMF hyphae are likely important contributors to current SOM turnover in sub-boreal systems. Our results also suggest that any increased EMF biomass that might result from higher below-ground C allocation by trees would not suppress C fluxes from sub-boreal soils. temperate and boreal forests represent a large carbon (C) sink, with an estimated half a gigatonne of C sequestered in above ground biomass every year (Myneni et al. , 2001). Much of this C is eventually transferred below ground, either after the death or annual senescence of vegetation or directly via photosynthetic transfer to root systems and associated ectomycorrhizal fungi (EMF). [SEP] Justify:

Output: The text corroborates the claim by stating that ectomycorrhizal fungi (EMF) and saprotrophic fungi contribute to carbon cycling through soil organic matter (SOM) decomposition. It specifically mentions that the study shows EMF hyphae are likely important contributors to current SOM turnover and also discusses the enzyme activities associated with organic matter degradation, which aligns with the claim that fungi play a role in nutrient cycling.


Input: Task: contrast the claim using the given text. Claim: Baobab trees can live for over 1,000 years and store large amounts of water in their trunks. [SEP] Text: Moreover, important size differences between specimens growing on the same site were reported; these dissimilarities were considered to be mainly of genetic origin (Breitenbach 1985; Wickens and Lowe 2008). Our study on the Platland tree demonstrates, once again, how questionable the size–age relation is and the large errors it can generate. According to complete measurements and dating results, the very large stem I is considerably younger than the smaller stem II (overall wood. 5 vs. 136. 7 m3; age 750 vs. 1,060 years). A preliminary genetic research (Catana and Patrut, unpublished results) suggested that the two stems of Platland tree possess identical DNA and belong to a single individual. The two stems have also grown on the same site. In this case, we need an explanation for the unexpected finding that a stem which is almost three times bigger is, however, over 300 years younger than the smaller stem of the same tree. Researchers of tall and large tree species around the world learned from their field experience that the largest specimens are usually not also the oldest. Typically, the largest specimens are those which had grown very fast when they were young and, eventually, continued their rapid growth (Hartesveldt et al. 1975). The research reported here demonstrates that such statements might be also valid for the African baobab. Our research on radiocarbon dating of baobabs (Patrut et al. 2010c), as well dating results presented by other researchers (Swart 1963; Woodborne et al. 2010) show that very large specimens are not necessarily among the oldest trees and that medium-sized individuals can also be very old. If one considers that the largest baobabs are those which grew very fast over their first ca. 100 years of life and maintained their rapid growth, we may have an explanation for our results on the two stems of Platland tree. In this case, stem I had probably grown much faster and is also considerably bigger, as the climate conditions in the area over its first century of life (ca. ad 1260–1360) were probably much more favorable for baobabs than over the first century of stem II (ca. ad 950–1050). 4. 3 tree systemAccording to dating results, the present trunk of the Platland tree comprises two stems of different ages. Therefore, morphologically, it can be considered a double tree. [SEP] Justify:

Output: The text contrasts the claim by explaining that size does not correlate with age in baobab trees. It details research findings indicating that large specimens of baobabs may be younger than smaller ones, which challenges the notion that large baobabs are necessarily old. It mentions that the Platland tree's two stems differ significantly in age despite being part of the same individual, and this suggests that ancient age is not exclusive to larger trees, thus contradicting the claim that baobabs live over 1,000 years based solely on size.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: TDR refers to the 'targeted dimensionality reduction' of Mante et al. (2013) shown in Figure 1f–h. LDA stands for linear discriminant analysis, but this column applies to any classification method (e. g. support vector machine, ordinal logistic regression, etc. ). All classification methods can be used to summarize population tuning via a time-dependent classification accuracy (e. g. Meyers et al. , 2012). PCA stands for principal component analysis, as shown in Figure 1i–k. FA stands for factor analysis, GPFA for Gaussian process factor analysis (Yu et al. , 2009), LDS for hidden linear dynamical system (Buesing et al. , 2012a; 2012b), jPCA is the method introduced in Churchland et al. (2012). Some of the existing methods can be extended to become more general, but here we refer to how these methods are actually used in the original research. Rows: The first two rows are the two defining goals of dPCA. Following rows highlight notable features of other methods. Signif. task parameters into account & provides summary statistics of population tuning✓✓✓✓Allows to reconstruct neural firing (captures variance)✓✓✓✓✓Based on dynamical model✓✓Based on probabilistic model✓✓✓Takes spike trains as input✓✓ Relationship to other methods, including our earlier work The method presented here is conceptually based on our previous work (Machens, 2010; Machens et al. , 2010; Brendel et al. , 2011), but is technically very different. The original approach from Machens et al. (2010) only works for two parameters of interest, such as time and stimulus. Machens (2010) suggested a partial generalization to multiple parameters and Brendel et al. (2011) introduced the full covariance decomposition and developed a probabilistic model. However, all of them imposed orthogonality on the decoder/encoder axes (and as a result did not distinguish them), a constraint that cannot be easily relaxed. While we have previously argued that orthogonality is a desirable feature of the decomposition, we now believe that it is better not to impose it upfront. [SEP] Justify:

Output: The text does not address the claim about memory recall with the right stimulus. It discusses various dimensionality reduction methods and their applications in neural firing reconstruction without making any claims regarding memory or recall accuracy.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: These soils store up to three times the amount of C as is stored in the above ground vegetation, primarily in the form of soil organic matter (SOM) (Schmidt et al. , 2011). sinks have the potential to become C sources under the projected global climate change (IPCC, 2007). Although some climate models assume increased C storage in temperate forest soils because of the increased plant allocation of photosynthetic C to roots and symbiotic fungi (Drigo et al. , 2010; Orwin et al. , 2011; Clemmensen et al. , 2013), there is a high degree of uncertainly in this assumption (Higgins and Harte, 2012). allocation with elevated CO2 may not only be short-lived (Norby et al. , 2010), but concomitant increases in mycorrhizal and mycorrhizosphere activity may stimulate decomposition of previously recalcitrant SOM (Cheng et al. , 2012; Phillips et al. , 2012b). Fundamental gaps in our understanding of terrestrial C cycling, including the relative contributions of EMF and other fungi to the release of SOM-C (Bargett, 2011), limit the predictive capability of current models (Von Lützow and Kögel-Knabner, 2009; Hayes et al. , 2012). Fungi, especially saprotrophic fungi, are thought to dominate organic matter turnover in temperate forest soils (Baldrian, 2008). Certain groups of saprotrophic fungi, specifically the white rot fungi, have unique abilities to degrade wood because they possess numerous copies of class II peroxidases (Floudas et al. , 2012). Both saprotrophic and EMF species, however, produce a range of hydrolytic and oxidative enzymes that have the potential to break down C-containing compounds and mobilize nutrients from SOM (Courty et al. , 2010; Floudas et al. , 2012). Although photosynthates are likely the primary source of C used by EMF under ideal conditions (Treseder et al. , 2006; Wolfe et al. , 2012), current research suggests that these fungi may also directly (Vaario et al. , 2012) or indirectly (Rineau et al. , 2012) access SOM-C pools. [SEP] Justify:

Output: The text corroborates the claim by stating that 'Fungi, especially saprotrophic fungi, are thought to dominate organic matter turnover in temperate forest soils,' which directly supports the idea that certain fungi decompose organic matter. Additionally, it mentions that both saprotrophic and EMF species produce enzymes that can break down C-containing compounds and mobilize nutrients from soil organic matter (SOM), reinforcing the role of fungi in nutrient cycling.


Input: Task: pass the claim using the given text. Claim: Jupiter is so massive that it doesn’t orbit the Sun directly but instead orbits a point just outside the Sun called the barycenter. [SEP] Text: 닫기 논문명 참고문헌 신청 검색조건추가 참고문헌 신청이 불가능한 논문이 있습니다. 입력된 URL을 확인해 주세요. 취소 다음 확인 이용수. . . 닫기 최근3년 최근1년 최근6개월 연도 월 이용수 정기구독 정책 구독 상품을 부정한 방법으로 이용시 구독 서비스 제공을 중단할 수 있습니다. 하나의 계정으로 중복로그인 한 경우 먼저 로그인 되어있던 계정은 자동 로그아웃 됩니다. [SEP] Justify:

Output: The text is irrelevant to the claim as it contains administrative content with no mention of Jupiter, its mass, or its orbital mechanics. Therefore, it does not corroborate or contrast with the claim.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: First, by looking across many datasets, we have learnt that encoding subspaces can sometimes be highly non-orthogonal (Figures 3–6e) and hence not demixable under orthogonality constraints. Second, by not imposing orthogonality, we can easier identify components that are truly orthogonal. Third, removing the orthogonality constraint allowed us to obtain a simple analytical solution in terms of singular value decompositions (see Materials and methods) and hence to avoid local minima, convergence issues, and any additional optimization-related hyperparameters. To demonstrate these advantages, we ran the algorithm of Brendel et al. (2011), dPCA-2011, on all our datasets. The resulting components were similar to the components presented here, with the amount of variance captured by the first 15 components being very close; but the achieved demixing was worse. For each component we defined a demixing index (see Materials and methods) that is equal to 1 if the component is perfectly demixed. For all datasets, these indices were significantly higher with our current dPCA-2015 method than with dPCA-2011. Moreover, dPCA-2011 failed to find some weak components at all. For comparison, see Figure 14 in the Materials and methods. Another method, called 'targeted dimensionality reduction' (TDR) has recently been suggested for neural data analysis and is similar in spirit to dPCA in that it looks for demixing linear projections (Mante et al. , 2013). As mentioned above, the original application of this method yields only one component per task parameter and ignores the condition-independent components. While TDR can be extended in various ways to yield more components, no principled way of doing it has been suggested so far. Comparison of dPCA with TDR on our datasets shows that dPCA demixes the task-parameter dependencies better than TDR (see Figure 14 in the Materials and methods). For an in-depth discussion of the relationship between dPCA and LDA/we refer the reader to the Methods. Briefly, LDA is a one-way technique, meaning that only one parameter (class id) is associated with each data point. Therefore, LDA cannot directly be applied to the demixing problem. [SEP] Justify:

Output: The text is largely technical and discusses methods related to dimensionality reduction and data analysis, specifically between dPCA and TDR. It does not address the claim about memory recall or imply any relationship to conditions under which memory is recalled. Therefore, it 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: Recalcitrant SOM, including lignin and humic complexes, is predominantly found in soil horizons dominated by EMF mycelial networks (Lindahl et al. , 2007). These mycelia are present at up to 600 kg of hyphae per hectare (Wallander et al. , 2001; Hendricks et al. , 2006) and account for up to one-third of the total microbial biomass in coniferous forests (Högberg and Högberg, 2002; Cairney, 2012). Although EMF mats are known to increase total soil CO2 respiration (Phillips et al. , 2012a), the full contribution of these fungi, and their extensive mycelia, to decomposition processes remains unknown (van der Wal et al. , 2012). Given the current interest in the C sequestration potential of boreal and temperate forests (and Boisvenue, 2012), as well as the influence of any increased C allocation below ground by plants, the role of EMF mycelia in SOM-C turnover warrants increased investigation (Talbot et al. , 2008; Ekblad et al. , 2013). Current research to estimate the extent and activity of EMF mycelia rely on methods to exclude roots and most saprotrophic fungi from a given zone in the soil profile. Hyphal in-growth cores or bags are commonly used (Wallander et al. , 2001). These hyphae traps are usually filled with an inert substrate, such as sand, that minimizes colonization by saprotrophic fungi (Korkama et al. , 2007; Lindahl et al. , 2007; Wallander et al. , 2010). In some cases, additional nutrient sources are added to ensure sufficient hyphal colonization or to evaluate the impact of limiting nutrients on EMF biomass production (Hagerberg and Wallander, 2002; Hagerberg et al. , 2003; Hedh et al. , 2008; Potila et al. , 2009; Berner et al. , 2012). However, the captured extramatrical hyphae may not accurately represent the composition or abundance of EMF communities in the surrounding soil (Hendricks et al. , 2006; Kjøller, 2006). Further, enzyme expression and activity is downregulated when no nutrients are present (Wright et al. , 2005). [SEP] Justify:

Output: The text corroborates the claim by discussing the role of ECM fungi (EMF) mycelial networks in soil, which are known to contribute to organic matter decomposition and carbon cycling processes. It notes that EMF mycelia account for a substantial portion of microbial biomass and suggests that their contribution to decomposition processes merits further research. This indicates an understanding of the fungi's potential role in nutrient cycling.


Input: Task: pass the claim using the given text. Claim: Saturn’s rings are composed mostly of ice particles, with some rock and dust mixed in. [SEP] Text: The maximum and minimum of the measurements at each level are shown by black lines. Contributions from the seven tagged sources in CTRL are shown by colored dashed lines. dust mass concentrations are derived from measured calcium and sodium concentrations. The measurement data are processed using the same method as Breider et al. (2014). Briefly, we assume a calcium to dust mass ratio of 6. 8 % and further correct the calcium concentrations for sea salt by assuming a calcium to sodium ratio of 4 %. Only measurements obtained north of 60∘ N are used for the analyses. The low-altitude observations near Fairbanks, Barrow, and are removed. Also, data from below 1 km on 1, 4, 5, and 9 July are removed to exclude the influence of wildfire. flight campaign was conducted in 2008, while the modeled vertical profiles are averaged for each April and July from 2007 to 2011, respectively. Zwaaftink et al. (2016), the simulation profiles are averaged for the regions north of 60∘ N and 170 to 35∘ W in April and 135 to 35∘ W in July. Also, the observations have a cutoff size of 4 µm and are thus only compared with simulated dust concentrations in the same size range. The pink shading in each panel represents the standard deviation with respect to time and space for the simulated total dust concentrations. 5Comparison of seasonal -retrieved (Luo et al. , 2015a, b; Yang et al. , 2022) (black solid line; with gray shading representing uncertainty) and model-simulated (pink solid line; with pink shading representing year-to-year variability) dust extinction vertical profiles in the Arctic (above 60∘ N). Contributions from seven tagged sources are shown by colored dashed lines. retrievals are for the year 2007 to 2009, while the model results are averaged over the same years. The uncertainties of the retrievals are assumed to be 20 % following Yang et al. (2022). Download Finally, we evaluate the simulated dust extinction against the Cloud– () retrieval (Luo et al. , 2015a, b; Yang et al. , 2022), which includes nighttime dust extinction for the period of 2007 to 2009. This dataset has improvements in dust separation from other aerosol types and thin dust layer detection in the Arctic compared to the standard Cloud–with () level 2 product (Winker et al. , 2013). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses measurement techniques related to dust extinction in the Arctic and does not mention Saturn's rings or their composition. Therefore, it does not provide any support, contrast, or relation to the claim.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: While LDA could be generalized to deal with several parameters in a systematic way, such a generalization has not been used for dimensionality reduction of neural data and does not have an established name in the statistical literature (we call it factorial LDA). We believe that for the purposes of dimensionality reduction, dPCA is a superior approach since it combines a reasonably high class separation with low reconstruction error, whereas LDA only optimizes class separation without taking the (potential) reconstruction error into account (see Figure 2). on the other hand, is a statistical test closely related to LDA that deals with multiple parameters. However, it deals with isolating the contribution of each parameter from residual noise rather than from the other parameters, and is therefore not suited for demixing. Limitations and future work While we believe that dPCA is an easy-to-use method of visualizing complex data sets with multiple task parameters, several limitations should be kept in mind. First, dPCA as presented here works only with discrete parameters, and all possible parameter combinations must be present in the data. This limitation is the downside of the large flexibility of the method: apart from the demixing constraint, we do not impose any other constraints on the latent variables and their estimation remains essentially non-parametric. In order to be able to treat continuous parameters or missing data (missing parameter combinations), we would need to further constrain the estimation of these latent variables, using e. g. a parametric model. One simple possibility is to directly use a parametric model for the activity of the single neurons, such as the linear model used in Mante et al. (2013), in order to fill in any missing data points, and then run dPCA subsequently. Second, the number of neurons needs to be sufficiently high in order to obtain reliable estimates of the demixed components. In our datasets, we found that at least ∼100 neurons were needed to achieve satisfactory demixing. The number is likely to be higher if more than three task parameters are to be demixed, as the number of interaction terms grows exponentially with the number of parameters. This trade-off between model complexity and demixing feasibility should be kept in mind when deciding how many parameters to put into the dPCA procedure. [SEP] Justify:

Output: The text is largely focused on discussing dimensionality reduction methods in neural data processing, specifically comparing LDA and dPCA, as well as limitations of certain statistical approaches. It does not address memory recall or the conditions under which memory can be perfectly recalled, making it 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: In order to estimate the contribution of EMF to the terrestrial C cycle, the potential SOM-degrading enzyme activity in locally abundant EMF hyphae needs to be determined. To investigate the relative contribution of EMF mycelia to enzyme activities in a sub-boreal spruce forest, we buried mesh bags filled with sterilized organic material with or without a surrounding sand barrier. Bags with a sand barrier were served to trap enzymatically active EMF hyphae while excluding obligately saprotrophic hyphae. Filling the mesh bags with fermentation-humic (FH) substrate taken from the same soil in which the traps were to be buried enabled a direct comparison between the EMF hyphae in the mesh bags and the surrounding FH layer. Based on our previous research, which showed that EMF root tips in these ecosystems had high hydrolytic and oxidative enzyme activities (Jones et al. , 2010, 2012), we hypothesized that EMF-dominated communities would have a SOM-degradation potential comparable to that of saprotroph-dominated communities. High-throughput sequencing, which combined with comprehensive measures of enzymes involved in SOM turnover, allowed us to evaluate the relative abundance and structure of fungal communities and to elucidate the potential role of EMF fungi in the soil C cycle. Materials and methods Field site The field site was part of a Ministry of Forests study that investigated the impact of fertilization on forest productivity and below-ground ecosystem processes (Brockley and Simpson, 2004). The current study occurred at the site, established in 1994 near Houston, British Columbia, Australia (54° 20′ 126° 17′) in the sub-boreal spruce biogeoclimatic zone, on Brunisol to Orthic Humo-soils. Each treatment within the site consisted 0. 164 hectare plots replicated three times. The current study utilized the non-fertilized control and two fertilized (annually and every 6 years) treatments; for further site information refer to Brockley and Simpson (2004). During the season of this study, the FH layer averaged 33% total C, 1. 06% total N, 64% organic matter, NH4-N, NO3-N and available P of 46, 2. 9 and 346 mg kg−1, respectively, and pH(CaCl2) 4. 35 (Ministry of ). [SEP] Justify:

Output: The text corroborates the claim by discussing the role of EMF (ectomycorrhizal fungi) in the degradation of soil organic matter (SOM) and their potential contribution to the terrestrial carbon cycle. It mentions the study's hypothesis that EMF-dominated communities would have a SOM-degradation potential comparable to that of saprotroph-dominated communities, indicating that these fungi are involved in decomposing organic materials and contributing to nutrient cycling.


Input: Task: contrast the claim using the given text. Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: The fairly low number of endemics in the might be attributed to their land-bridge characteristics, as is also the case for the Islands [24]. However, the recent isolation of the might also be a determining factor explaining the low richness in endemic species. Islands were isolated from the mainland during the Pleistocene, or more recently, whereas the palaeogeographic evolution of the started about 23–12 million years ago [15]. Thus, there has been less time for the speciation and evolution of new species on the Ionian Islands. Most of the Ionian endemics have probably evolved before the isolation of the islands, resulting also to the low single island endemic species richness observed in the Ionian Islands. The low number of regional endemics in the small could be attributed to both increased competition and increased stochastic extinction risks. In contrast, endemics are also found on islands of the Aegean that are three orders of magnitude smaller than in the Ionian. However, the isolation and size of the islands only partially explain the difference in the number of endemics between the two archipelagos, because Greek endemics are absent from small compared to similar sized islands in the Aegean. Furthermore, if recent isolation is the only mechanism responsible for this difference, the absence of Greek endemics would not be expected. An alternative explanation might be the role of competition among species serving as a limiting factor. Precipitation levels are higher in the Ionian compared to the Aegean. Consequently, vegetation cover is higher and much denser in the Ionian compared to the Aegean. For instance, small islets are typically barren in the Aegean, whereas similarly sized islets in the Ionian are densely vegetated. This dense vegetation is primarily dominated by a few, common woody species that competitively exclude more ‘ecologically fragile’ and marginal endemic species. Consequently, most Ionian endemics tend to persist on rocky cliffs, which are areas of low interspecific competition. Another explanation for the difference in endemism levels between the two archipelagos might be based on the availability and diversity of suitable habitats for endemics. The habitats available to endemics is more restricted in the Ionian compared to the Aegean. The maximum elevation of islands hosting regional and single island endemic species is much lower in the compared to the Ionian ones (Table 1). [SEP] Justify:

Output: The text contrasts the claim by discussing the factors leading to low endemism in the Ionian Islands, suggesting that despite certain regions having varying levels of endemism, they do not fulfill the criteria of high species diversity and endemism mentioned in the claim. The references to lower richness in endemic species and competitive exclusion indicate that these areas might not be considered biodiversity hotspots.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In cases when there are many task parameters of interest, dPCA is likely to be less useful than the more standard parametric single-unit approaches (such as linear regression). As a trivial example, imagine that only N=1 neuron has been recorded; it might have strong and significant tuning to various parameters of interest, but there is no way to demix (or decode) these parameters from the recorded 'population. ' Third, even with a large number of neurons, a dataset may be non-demixable, in which case dPCA would fail. For instance, if the high-variance directions of the stimulus and the decision parts of the neural activities fully overlap, then there is no linear decoder that can demix the two parameters. Finally, dPCA components corresponding to the same parameter (e. g. successive stimulus components) are here chosen to be orthogonal, similarly to PCA. This can make successive components difficult to interpret (e. g. the second and the third stimulus components in Figure 3). To make them more interpretable, the orthogonality constraint could be replaced with some other constraints, such as e. g. requiring each component to have activity 'localized' in time. This problem may be addressed in future work. Materials and methods We will first explain the dPCA algorithm in the most well-behaved case of simultaneously recorded and fully balanced data. A dataset with categorical predictors is called balanced when there is the same number of data points for each combination of predictors; in our case this means that there is the same number of trials for each combination of task parameters. This scenario is unlikely in most practical applications where the experimenter often does not have full control over some of the task parameters (such as e. g. animal’s decisions). Our suggestion for unbalanced datasets is to use what amounts to a 're-balancing' procedure as explained below. Finally, we will deal with the case of sequentially recorded neurons (all datasets analyzed in this manuscript fall into this category). Mathematical notation Request a detailed protocol In each of the datasets analyzed in this manuscript, trials can be labeled with two parameters: 'stimulus' and 'decision'. Note that a 'reward' label is not needed, because its value can be deduced from the other two due to the deterministic reward protocols in all tasks. [SEP] Justify:

Output: The text is irrelevant to the claim regarding memory recall. It discusses the dPCA algorithm and challenges in decoding parameters from neural activity data, without addressing the ability to perfectly recall memories or the influence of stimuli on memory recall.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Hyphal trap bags were buried at the site on 5 June 2009. Each set of bags (with or without sand barriers, +SB and –SB, respectively) were placed in the FH layer of the treatment plots from which the substrates were originally taken. One pair of +SB and –SB bags was buried at five random spots within each plot, 60 cm from the base of a tree and 60 cm from each other. All bags and an FH sample (10 × 10 cm) from between each set were harvested on 1 October 2009. Upon return to the lab, the five +SB, −SB or FH sub-samples from individual plots were composited, so that a single representative +SB, −SB and FH sample was obtained per plot. Sand from the +S mesh bags was also composited on a plot basis. Half of each composited sample was frozen at −20 °C for subsequent molecular analysis and half was stored at 4 °C overnight for use in enzyme assays. Enzyme assays All +SB, −SB, FH, sand and residual-activity control samples, as well as EMF tips selected from the FH samples, were assessed for their organic matter breakdown potential. The assessed enzymes included β-1,4-glucosidase (EC 3. 2. 1. 21), cellobiohydrolase (EC 3. 2. 1. 91), β-1,4-xylosidase (EC 3. 2. 1. 37), peroxidase (EC 1. 11. 1. 7), laccase (EC 1. 10. 3. 2), phenol oxidase (EC 1. 14. 18. 1 and EC 1. 10. 3. 2), β-1,4-N-acetylglucosaminidase (EC 3. 2. 1. 52), acid phosphatase (EC 3. 1. 3. 2), leucine aminopeptidase (EC 3. 4. 11. 1) and sulfatase (EC 3. 1. 6. 1). [SEP] Justify:

Output: The text corroborates the claim by detailing enzyme assays that were conducted to assess the organic matter breakdown potential of various samples, including those taken from fungal habitats (FH samples). The mention of specific enzymes, such as β-1,4-glucosidase and peroxidase, suggests that fungi play a role in decomposing organic matter, which is integral to nutrient cycling.


Input: Task: contrast the claim using the given text. Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: IntroductionFungi are an essential functional component of terrestrial ecosystems as decomposers, symbionts and pathogens (Mueller et al. 2007) and they represent one of the most biodiverse groups of organisms on earth (Hawksworth 1991, 2001). However, our knowledge of their diversity and ecological function in Neotropical lowland forests is limited. The ecological interaction of macrofungi with other organisms in these forests is poorly understood due to the largely unexplored, but likely huge, fungal diversity, as well as the cryptic and ephemeral nature of many fungal species. Incomplete information on the biodiversity of macrofungi from such ecosystems is only available from scattered sources (Lodge and Cantrell 1995; Lodge 1997; Jiménez-Valverde and Hortal 2003; Piepenbring 2007; Schmit and Mueller 2007; Swapna et al. 2008). A major part of the global but unknown fungal biodiversity is assumed to occur in tropical regions, where the diversity of fungi may be higher than in temperate regions, because of more favorable environmental conditions throughout the year, a higher diversity of vascular plants that create niches and microhabitats for fungi, and the presence of many ecotones (Hawksworth 2001; Kark 2007). The diversity of macrofungi in tropical forests as assessed by Lodge et al. (1995) showed that the highest diversity in the Neotropics occurred in the Amazon basin with aphyllophoralean, pyrenomycetous, xylariaceous and hyphomycetous fungi being most species rich. rainforest is arguably the most species-rich terrestrial ecosystem in the world (Hoorn et al. 2010). Biodiversity studies in Amazon forests have focused mainly on plants, especially tree species (Gentry 1988a; Duivenvoorden 1996; Pitman et al. 2001; Condit et al. 2002) and revealed that these forests hold a very high number of plant species (Gentry 1988a; Valencia et al. 1994; Rudas and Prieto 1998; et al. 2003; Duque 2004; Hoorn et al. 2010). Despite this extensive plant and animal biodiversity, the region is not yet recognized as a biodiversity hotspot (Myers et al. 2000) ( December 2009). The study of fungal biodiversity in tropical lowland rain forests is urgent as these are rapidly decreasing due to deforestation (Brown et al. [SEP] Justify:

Output: The text contrasts the claim by stating that despite the Amazon basin's high biodiversity and being described as the most species-rich terrestrial ecosystem, it is not officially recognized as a biodiversity hotspot, as indicated by the reference to Myers et al. (2000). This suggests a divergence from the claim that categorizes the Amazon as a biodiversity hotspot.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Let us define the following marginalized averages: x¯=⟨xtsdk⟩tsdk=x¯⋅⋅⋅⋅x¯t=⟨xtsdk−x¯⟩sdk=x¯t⋅⋅⋅−x¯⋅⋅⋅⋅x¯s=⟨xtsdk−x¯⟩tdk=x¯⋅s⋅⋅−x¯⋅⋅⋅⋅x¯d=⟨xtsdk−x¯⟩tsk=x¯⋅⋅d⋅−x¯⋅⋅⋅⋅x¯ts=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩dk=x¯ts⋅⋅−x¯t⋅⋅⋅−x¯⋅s⋅⋅+x¯⋅⋅⋅⋅x¯td=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩sk=x¯t⋅d⋅−x¯t⋅⋅⋅−x¯⋅⋅d⋅+x¯⋅⋅⋅⋅x¯sd=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩tk=x¯⋅sd⋅−x¯⋅s⋅⋅−x¯⋅⋅d⋅+x¯⋅⋅⋅⋅x¯tsd=⟨xtsdk−x¯−x¯t−x¯s−x¯d−x¯ts−x¯td−x¯sd⟩k=x¯tsd⋅−x¯ts⋅⋅−x¯⋅sd⋅−x¯t⋅d⋅=+x [SEP] Justify:

Output: The text does not address the claim about memory recall and instead presents a mathematical definition involving marginalized averages that appears unrelated to the psychology of memory. Therefore, it 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: Data are provided as means (n=9) with s. d. in brackets. EMF tips, matching sequences belonged to unidentified ectomycorrhizal tips. aThe most common (>0. 5% relative abundance) fungal groups are given at the genus level. Fungal functional diversity The presence of a sand barrier had a significant impact on fungal groups colonizing the hyphal traps. Hyphal traps with a sand barrier were primarily colonized by EMF, whereas hyphal traps without a sand barrier were equally colonized by saprotrophs and EMF (Figure 2a). As previously noted, −SB traps were heavily colonized by Mortierella spp. , which comprised the majority of the identified saprotrophs. The trophic groups that colonized the +SB traps were highly comparable to those in the surrounding FH layer (Figure 2b), with the two groups clustering at ∼98% similarity. Both of these communities contained significantly lower populations of known saprotrophs than the −SB community (Figure 2c). Figure 2Fungal functional group diversity in the different hyphal trap bags, sand barriers and 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; Sand, sand barrier; n=9 each. (a) The relative abundance of the different ecological functional groups in each compartment. The total number of contributing sequence reads is indicated next to each histogram. (b) Cluster analysis of functional group diversity, indicating the percent similarity (based on Pearson’s r correlation) of communities in the different compartments. (c) Differences in the abundance of each functional group. Communities in the same row with different letters are different at the indicated significance level; NS, not significant. Other mycorrhizae: ericoid, arbutoid, monitropoid and arbuscular mycorrhizae; mixed functional groups: belong to more than one functional group. Unknown fungi indicates that the best matching sequence belonged to the kingdom Fungi. Open in new tabDownload slide Ectomycorrhizal tip diversity A total of 226 EMF tips were selected, based on abundant morphotypes, from the FH layer adjacent to the hyphal trap bags. [SEP] Justify:

Output: The text corroborates the claim by discussing various fungal groups, including saprotrophs, which are known for their role in decomposing organic matter. It highlights the presence and functional diversity of these fungi in specific environments, indicating their contribution to nutrient cycling as they colonize various substrates, such as hyphal traps with and without sand barriers.


Input: Task: contrast the claim using the given text. Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: Country-level endemism showed that Loja and Guayas had most endemics in Ecuador (12 and 11 species, respectively), and Tumbes (9 species) in Peru. The ratio woody SDF endemics versus total vascular plant endemics showed that Tumbes had a substantial percentage of the endemics reported for that department in the SDF vegetation. Woody SDF endemics per 1,000 km2 of the study area were highest in Loja, Tumbes and (Table 3). Collection intensity, i. e. , the number of collections per species of woody plants in the SDFs, has been highest in Guayas (ca. eight collections per species), Tumbes (ca. six collections per species) and Manabí (ca. five collections per species). Lowest collection intensity was in the SDFs of Piura. Table 3 Species and endemism numbers for provinces and departments with seasonally dry forests in western Ecuador and northwestern PeruFull size table DiscussionPatterns of species richness, endemism and distributionIn the first comprehensive review of the floristics of neotropical SDF (1995) noted that SDF ecosystems were less species rich and contained only a subset of the plant diversity found in the more humid forests. The lower diversity in the SDFs is clearly due to the low levels of diversity within families and genera. A notable exception is Leguminosae. This family showed high levels of diversity at the generic (34 genera, 19% of the total), specific (70 species, 22% of the total) and endemic species level (15 endemics, 21% of the total). This is not surprising since several studies have shown that this family is among the most, if not the most, prominent members of SDF in the Neotropics (Gentry 1995; Pennington et al. 2006). Malvaceae, on the contrary, are not necessarily regarded as important constituents of tropical dry forest communities (Pennington et al. 2006). [SEP] Justify:

Output: The text contrasts the claim by discussing ecosystems with lower levels of species diversity, specifically seasonally dry forests (SDF). It notes that these ecosystems are 'less species rich’ and only contain 'a subset of the plant diversity found in the more humid forests,' which suggests that they do not meet the criteria typically associated with biodiversity hotspots like the Amazon rainforest.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: ¯t⋅⋅⋅+x¯⋅s⋅⋅+x¯⋅⋅d⋅−x¯⋅⋅⋅⋅ϵtsdk=xtsdk−⟨xtsdk⟩k=xtsdk−x¯tsd⋅. [SEP] Justify:

Output: The text does not provide any information related to the claim about memory recall. It appears to be a mathematical expression or sequence that lacks relevance to the assertion regarding memory and stimuli.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Sequencing of the complete ITS region confirmed EMF tip identity at the following relative abundances: Cenococcum (24%), Piloderma (24%), Amphinema (17%), Tylospora (12%), Cortinarius (12%), Hygrophorus (5%) and Hysterangium (3%) spp. Approximately half of the Cenococcum tips exhibited multiple banding patterns after PCR amplification of the ITS region. Band-specific sequencing confirmed that these tips contained Cenococcum sequences in association with Phialocephala, Piloderma, Mycena or Cadophora spp. sequences (S4). For the purposes of enzyme activity assays (below), these tips were considered as Cenococcum based on the distinctive mantle formed by this fungus. Activity of enzymes associated with SOM breakdown The hydrolytic and oxidative enzyme activity potentials were assessed for all components of the hyphal trap systems, the adjacent FH layer and residual-activity controls (Table 3), and for EMF tips sampled from the FH layer (Table 4). Residual enzyme activity was detected for most enzymes in the gamma-irradiated control soils, which were kept under dry and sterile conditions in the lab. This residual activity was generally lower than that in the field samples, however, β-1,4-xylosidase and cellobiohydrolase activities were not statistically different from the hyphal traps (Table 3). Exploratory PCA analysis showed that activity patterns in the control samples were distinct from those in the field samples (S5), indicating that active fungi had a dominant influence on enzyme activity under the study conditions. These differences in magnitude and pattern, combined with the likelihood that residual enzymes would have been degraded under non-sterile field conditions (Allison, 2006), informed the decision not to subtract the control from field enzyme activities. Table 3 Open in new tab Potential organic matter breakdown activity in the hyphal trap bags, associated sand, adjacent forest floor and residual-activity controls Enzyme. Enzyme activity (μmol h−1 g−1). . General role. −SB. FH. +SB. Sand. Control. Acid phosphatasePhosphate mobilization3. 06 (0. 51) b5. 11 (1. 06) a2. 53 (0. 80) b<0. 01 (0. [SEP] Justify:

Output: The text corroborates the claim by discussing enzyme activities associated with fungi, which indicates their role in the breakdown of organic matter. The mention of 'hydrolytic and oxidative enzyme activity potentials' and their connection to 'organic matter breakdown' directly supports the idea that certain fungi contribute to nutrient cycling through decomposition.


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: Tocher’s method partially agreed with the UPGMA method (Figure 1), since group II generated by the former had the same composition as group V obtained by UPGMA. Tocher’s group I included all other groups formed by the UPGMA method (groups and IV); however, if the average distance of 0. 78 had been used as a cutoff point, there would be 100% agreement between the two clustering methods. Both methods have been widely used in studies of populations; records of similarities between both have been reported by several authors (Büttow et al. , 2010Büttow. Caracterização Molecular de Populações de (Arecaceae) do Sul do Brasil através de Marcadores AFLP. De Fruticultura, 2010; 32:230-239. ; Campos et al. , 2010. Avaliação de Acessos de Mandioca do Banco de Germoplasma da Unemat Cáceres - Mato Grosso. Revista Trópica - Ciências Agrárias e Biológicas, 2010; 4(2): 44-54. ; Bertan et al. , 2014da, et al. Comparação de Métodos de Agrupamento na Representação da Distância Morfológica entre Genótipos de Trigo. Technology, 2014; 12(3): 279-286. ). Many specimens from the sampled area are drying and dying as a consequence of the drastic change in the natural environment of the species, which causes it to not adapt to the new microclimate conditions created after the disturbance. Forest fragmentation, which is a direct and inevitable consequence of deforestation, can affect the Amazon biome in many ways, altering the diversity and communities of the formed fragments, as well as changing ecological processes such as pollination, nutrient cycling and carbon stocking (Laurence & Vasconcelos, 2009). According to Tabarelli et al. (2012. A conversão da floresta atlântica em paisagens antrópicas: lições para a conservação da diversidade biológica das florestas tropicais. Interciencia, 2012; 37(2): 88-92. [SEP] Justify:

Output: The text does not directly corroborate or contrast the claim that biodiversity hotspots like the Amazon rainforest are characterized by high species diversity and endemism. Instead, it discusses methodologies for analyzing populations and notes the effects of environmental changes and deforestation on species. While it mentions alterations in diversity due to forest fragmentation, it does not specifically classify the Amazon rainforest or related regions as biodiversity hotspots, failing to connect to the claim's definition.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Here x¯ is simply the overall mean firing rate of our neuron, x¯t is the average time-varying firing rate once the overall mean has been subtracted, etc. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses neuronal firing rates and does not address the topic of memory recall or the influence of stimuli on memory.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 79 (0. 47) a<0. 01 (0. 00) b0. 21 (0. 40) bβ-1,4-GlucosidaseCellulose degradation2. 23 (0. 69) b3. 05 (0. 91) ab3. 44 (1. 12) a0. 01 (0. 00) c0. 70 (0. 56) cCellobiohydrolaseCellulose degradation0. 77 (0. 58) ab0. 87 (0. 56) ab1. 07 (0. 61) a<0. 01 (0. 00) c0. 36 (0. 20) bcβ-1,4-XylosidaseHemicellulose degradation0. 12 (0. 06) ab0. 22 (0. 18) a0. 19 (0. 10) a<0. 01 (0. 00) b0. 10 (0. 05) abLaccaseaLignin degradation13. 3 (12. 5) ab19. 0 (11. 3) a5. 4 (8. 3) bND4. 13 (4. 6) bPeroxidaseaLignin degradation19. 2 (15. 4) c39. 2 (18. 4) ab48. 9 (16. 5) aND27. 5 (12. 6) bcPhenol oxidaseaLignin degradation0. 5 (1. 3) b0. 7 (1. 1) b3. 5 (2. 6) aND1. 54 (1. 00) abAbbreviations: control, residual enzyme activity in irradiated controls (irradiated control bags not placed in the field); FH, in situ fermentation-humic layer; ND, no activity detected; −SB, hyphal trap bags with no sand barrier; +SB, hyphal trap bags with sand barrier; Sand, sand barrier. Data are provided as means (n=9) with s. d. in brackets. Data in the same row with different letters are significantly different at P<0. 01. aActivities given as mmol h−1 g−1. Table 4 Open in new tab Potential organic matter breakdown activity of ectomycorrhizal tips Enzyme. Enzyme activity (pmol min−1 mm−2). . [SEP] Justify:

Output: The text provides detailed data on enzyme activities related to the degradation of cellulose, hemicellulose, and lignin, which are critical processes in the decomposition of organic matter. Specifically, it lists various enzymes such as β-1,4-Glucosidase, Cellobiohydrolase, and others, which play vital roles in the breakdown of complex organic materials. This directly supports the claim that fungi contribute to nutrient cycling by 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: The first large worm lizard described from Angola was Lepidostsernon (Phractogonus) anchietae Bocage 1873, from ‘Humbe, dans l’intérior de Mossamedes’, later transferred to the genus Monopeltis (Boulenger 1885). Broadley et al. (1976) relegated Monard’s (1937) species M. okavangensis and M. devisi to M. anchietae, which is now known to have a wider range in northern Namibia and adjacent Botswana. Monopeltis vanderysti vilhenai, described by Laurent (1954) from Dundo, Angola, was not recognised by Broadley et al. (1976) and returned to M. vanderysti, which is widely distributed in the Congo region. specimen remains the only Angolan record of the species. Monopeltis capensis in Angola was first recorded by Bocage (1873), and later by Monard (1937). Although provisionally placed in the M. capensis capensis (Broadley et al. 1976), with a wide range through the Kalahari region (Northern Cape, South Africa, through Botswana to southern Angola), it was later treated as a separate species, M. infuscata Broadley 1997a. Monard’s (1937) species Monopeltis granti kuanyamarum, described from a single specimen from Mupanda, was transferred to Dalophia pistillum (Broadley et al. 1976). The only Angolan specimen of Dalophia ellenbergeri was collected whilst trench digging during hostilities at (Branch and McCartney 1992; Broadley 1997b). Gray (1865) described Dalophia welwitschii from Pungo Andongo, and this has not been rediscovered. It is the type species for the genus Dalophia, and Gans (2005) was obviously in error when treating it as M. welwitschii and yet still continuing to recognise Dalophia. A phylogeny of African amphisbaenids (Measey and Tolley 2013), albeit based on poor taxon sampling, recovered Monopeltis and Dalophia as monophyletic clades, supporting the use of Dalophia welwitschii. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses specific species of lizards from Angola and their classification, without any mention of biodiversity hotspots or the Amazon rainforest. Therefore, it does not provide any corroboration or contrast to the claim about biodiversity hotspots.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The right-hand side shows the same averaging procedure in the more explicit form using ANOVA-style notation, in which averages of x over everything apart from the explicitly mentioned parameters, e. g. , the stimulus s, are denoted by terms of the form x¯⋅s⋅⋅. One can directly see that the original neural activities are given by the sum of all marginalizations: xtsdk=x¯+x¯t+x¯s+x¯d+x¯ts+x¯td+x¯ds+x¯tsd+ϵtsdk. This decomposition is identical to the one used in factorial ANOVA (Rutherford, 2001; Christensen, 2011) where task parameters are called factors. literature uses a slightly different notation with task parameters (t,s,d,k) replaced by indices (i,j,k,l) and with Greek letters designating individual terms: xijkl=μ+αi+βj+γk+δij+ζjk+ηik+θijk+ϵijkl. We will use our notation, though, to keep the connection with the task parameters more explicit. For the purposes of demixing neural signals in the context of our datasets, we combine some of these terms together. Indeed, demixing a time-independent pure stimulus term x¯s from a stimulus-time interaction term x¯ts makes little sense because we expect all neural components to change with time. Hence, we group the terms as follows (without changing the notation): xtsdk=x¯+x¯t+x¯s+x¯ts⏟x¯ts+x¯d+x¯td⏟x¯td+x¯sd+x¯tsd⏟x¯tsd+ϵtsdk. Here the first term on the right-hand side is the mean firing rate, the last term is the trial-to-trial noise, and we call the other terms condition-independent term, stimulus term, decision term, and stimulus-decision interaction term. This decomposition is illustrated in Figure 8 for several exemplary neurons (we only show the decomposition of the PSTH part, leaving out the noise term). Figure 8 Download asset Open asset Marginalization procedure. [SEP] Justify:

Output: The text does not address the claim about perfect recollection of memory through stimulus. Instead, it discusses a technical averaging procedure and neural signal demixing in relation to task parameters and does not mention memory recall or stimulus as a factor in recollecting memories.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 05) enzyme activities associated with cellulose and hemicellulose degradation than did Piloderma or Cenococcum tips. The hydrolytic enzyme activity of these EMF tips was highly correlated with that of the FH layer from which they were sampled (Table 5). Both the average tip activity and the individually assessed activities of Amphinema and Piloderma spp. were positively associated with cellobiohydrolase, glucosidase and xylosidase activities within the surrounding FH layer. Table 5 Open in new tab Direct correlation (Spearmans r) between enzyme activities of the ectomycorrhizal tips selected from the FH layer of the forest floor and the activities of the FH layer. Enzyme. EMF tipsa. Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NS. Enzyme. EMF tipsa. Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NSAbbreviation: FH, in situ fermentation-humic layer. Correlations are significant at *P<0. 05, **P<0. 01. Note that only enzymes and EMF tips that were significantly related to FH are shown. aEMF tips, correlation with the average activity of all ectomycorrhizal tips. Table 5 Open in new tab Direct correlation (Spearmans r) between enzyme activities of the ectomycorrhizal tips selected from the FH layer of the forest floor and the activities of the FH layer. Enzyme. EMF tipsa. Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NS. Enzyme. EMF tipsa. [SEP] Justify:

Output: The text corroborates the claim as it discusses the enzyme activities of various fungi, such as Amphinema and Piloderma, which are positively associated with the degradation of cellulose and hemicellulose. The reference to hydrolytic enzyme activity suggests that these fungi play a significant role in the decomposition of organic matter, thereby contributing to nutrient cycling in their environment.


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: It would appear that perhaps as many as 75+ new lizard species await discovery in Angola, and that many of these will be endemic. Branch (2014) noted that endemicity in lizards was particularly evident in rupicolous forms (many geckos, cordylids and skinks) associated with rocky outcrops. Rock exposures may form an archipelago of ‘sky islands’ on which isolation inhibits gene flow and thus leads to speciation. It is the lizard families containing large numbers of rupicolous species, i. e. Gekkonidae, Cordylidae and Scincidae, already show the greatest levels of endemicity among Angolan reptiles, and in which recent surveys have already identified numerous cryptic taxa (Stanley et al. 2016; Branch et al. 2017). Fig. 13. 1Species accumulation curves for reptile discoveries in Angola (blue) and southern Africa (red) showing the relative stagnation of Angolan reptile species descriptions during the twentieth century. By the end of the nineteenth century 67. 4% of Angolan reptiles had already been described, in contrast to less than half (47. 8%) of those in southern AfricaFull size imageof ‘Hotspots’protected areas underperform in their stated conservation goals (Lindsey et al. 2014; Bowker et al. 2017). It is now generally accepted that modern national and internationally co-ordinated networks of protected area should be designed to cover important biodiversity hotspots and also protect habitats essential for the maintenance of ecosystem services such as water flow and quality, nutrient transfer, etc. (NPAES 2010). Such a revised Angolan network was proposed many years ago (Huntley 1974), and initial biodiversity surveys to gain insight into biodiversity in potential sites were undertaken (Huntley 2009; Huntley and Francisco 2015). Recent studies have shown that protected reserves designed to protect mammals, birds and amphibians are effective in protecting snakes, but fare badly in protecting African lizard diversity (Lewin et al. 2016; Roll et al. 2017). Future systematic biodiversity surveys should be directed to unique habitats and landforms in undersampled regions. Some of the interesting species recorded on recent field surveys are illustrated in Fig. 13. 2. Fig. 13. 2Angolan reptiles. Top to bottom, left to right. Bogert’s (Afroedura cf. [SEP] Justify:

Output: The text discusses the potential discovery of new lizard species in Angola and the endemicity of these species, but does not specifically reference biodiversity hotspots like the Amazon rainforest. While it highlights biodiversity and species diversity, it does not directly corroborate or contrast the claim regarding the definition of biodiversity hotspots.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: PSTHs of three exemplary neurons from the somatosensory working memory task decomposed into marginalizations. We apply this marginalization procedure to every neuron, splitting the whole data matrix 𝐗 into parts. Assuming from now on that the data matrix is centered (i. e. x¯=0 for all neurons), we can write the decomposition in the matrix form X=Xt+Xts+Xtd+Xtsd+Xnoise=∑ϕXϕ+Xnoise. Here t, ts, td, and tsd are labels and not indices, and all terms are understood to be matrices of the same N×KSQT size, so e. g. 𝐗t is not an N×T sized matrix, but the full size N×KSQT matrix with N×T unique values replicated KSQ times. Crucially, the marginalization procedure ensures that all terms are uncorrelated and that the N×N covariance matrix 𝐂=𝐗𝐗⊤/(KSQT) is linearly decomposed into the sum of covariance matrices from each marginalization (see 2. In the remaining discussion, it will often be sufficient to focus on the individual loss functions Lϕ, in which case we will drop the indices ϕ on the decoder and encoder matrices for notational convenience, and simply write 𝐅 and 𝐃. Without any additional constraints, the decoder and encoder are only defined up to their product 𝐅𝐃 of rank q. To make the decomposition unique, we will assume that 𝐅 has orthonormal columns and that components are ordered such that their variance (row variance of 𝐃𝐗) is decreasing. The reason for this choice will become clear below. This loss function penalizes the difference between the marginalized data 𝐗ϕ and the reconstructed full data 𝐗, i. e. , the full data projected with the decoders 𝐃 onto a low-dimensional latent space and then reconstructed with the encoders 𝐅 (see Video 2). [SEP] Justify:

Output: The text does not address the claim regarding the recall of memory with stimulus. Instead, it discusses a technical process involving the marginalization procedure and decomposition of data matrices related to neuronal activity. There is no mention or implication of memory recall or the effect of stimuli on memory, rendering the relationship between the text and the claim irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NSAbbreviation: FH, in situ fermentation-humic layer. Correlations are significant at *P<0. 05, **P<0. 01. Note that only enzymes and EMF tips that were significantly related to FH are shown. aEMF tips, correlation with the average activity of all ectomycorrhizal tips. Impact of fungal community structure on enzyme activity The relationships between fungal community structure and the activity of enzymes associated with organic matter breakdown were explored by PCA. Sand samples were not included as they exhibited little enzyme activity. Differences in enzyme activity patterns between the in situ FH layer and hyphal traps were best described by the first three PCA axes (Figure 3). Location along PCA axis 1 (PC1) was driven primarily by the activities of cellulose- and hemicellulose-degrading enzymes, which strongly overlapped in the FH layer and +SB traps. Activities in these communities were also strongly associated along PC3, reflecting similar patterns of lignin degradation (peroxidase) potential. The combined effects of the PC1- and PC3-loading factors resulted in a distinct separation of the −SB traps. Both FH and −SB differed from +SB along PC2, however, reflecting variation in enzymes associated with phosphorus release, lignin degradation (laccase versus phenol oxidase) and nitrogen release. Figure 3PCA ordination of potential enzyme activity in the hyphal trap bags and the FH layers of the forest floor. The proportion of variance explained by each axis is shown, and the contribution of each enzyme in the ordination is indicated by the plot vectors. −SB, hyphal trap bags with no sand barrier; FH, in situ fermentation-humic layer; +SB, hyphal trap bags with sand barrier. [SEP] Justify:

Output: The text corroborates the claim by discussing the relationship between fungal community structure and enzyme activity associated with organic matter breakdown. It highlights the significance of fungal enzymes, particularly those related to cellulose and lignin degradation, which are integral to the decomposition process and nutrient cycling in ecosystems.


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: Figure 5Distribution of endemic vascular plant species in general and of range-restricted endemic vascular plant species in Iran and its areas of endemism. Full size imageFigure 6Endemic vascular plant species of Iran in areas of endemism. Endemic vascular plant species restricted to an area of endemism are indicated in dark grey, whereas those shared with other areas of endemism are indicated in light grey, their numbers being given in the bars connecting pie charts corresponding to areas of endemism. Full size imageTable 5 Vascular plant endemism in the areas of endemism in Iran. Full size tableThe second-richest area is Alborz, which harbours 29% of all Iranian vascular plant endemics nearly half of them being restricted to Alborz (Figs 5, 6 and Table 5). If taking area size into account, Alborz is richer than other regions. This is particularly evident for range-restricted endemics (here species which are only in maximally three grid cells of 0. 5° × 0. 5°), where, compared to Zagros, Alborz has half the number of endemics on one fifth the area (Fig. 5; Table 5). Generally, has the highest concentration of endemics in the (Noroozi et al. 2018). Alborz is a narrow, but very high and contiguous east-west oriented mountain range (Fig. 1) that borders on lowland deserts in the south and on the in the north. A wide elevational range, high topographic complexity and strong environmental heterogeneity explain its high plant diversity and great richness of endemics19. Alborz connects Kopet Dagh-Khorassan mountains in the east with the and Zagros in the west (evident also in the high numbers of endemic species shared between Alborz and at least one of these regions; Fig. 6), thus acting as a corridor between and Caucasus plus Anatolian mountains. Therefore, Alborz not only harbours a high number of local endemics, but also many elements showing biogeographic connections east- and/or westwards. In the Azerbaijan Plateau, 21% of Iranian vascular plant endemics are found, half of which are endemic to this area (Figs 5, 6 and Table 5). Plateau has a fragmented orography caused by different tectonic and volcanic activities44. [SEP] Justify:

Output: The text discusses the distribution and characteristics of endemic vascular plant species in Iran, particularly in the Alborz region, but it does not mention biodiversity hotspots or make any reference to the Amazon rainforest. Therefore, it is irrelevant to the claim being analyzed as it provides no information supporting or contrasting it.


Input: Task: pass the claim using the given text. Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The loss function thereby favours variance in marginalization ϕ and punishes variance coming from all other marginalizations and from trial-to-trial noise. Given that the marginalized averages are uncorrelated with each other, we can make this observation clear by writing, Lϕ=∥Xϕ−FDX∥2=∥Xϕ−FDXϕ∥2+∥FD(X−Xϕ)∥2. Here the first term corresponds to the non-explained variance in marginalization ϕ and the second term corresponds to the variance coming from all other marginalizations and from trial-to-trial noise. The dPCA objective is to minimize both. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as of the dPCA algorithm. Illustration of the dPCA algorithm using the somatosensory working memory task. We note that the loss function Lϕ is of the general form ∥𝐗ϕ-𝐀𝐗∥2, with 𝐀=𝐅𝐃. For an arbitrary N×N matrix 𝐀, minimization of the loss function amounts to a classical regression problem with the well-known ordinary least squares (OLS) solution, 𝐀OLS=𝐗ϕ𝐗(𝐗𝐗)⊤-1⊤. In our case, 𝐀=𝐅𝐃 is an N×N matrix of rank q, which we will make explicit by writing 𝐀q. The dPCA loss function therefore amounts to a linear regression problem with an additional rank constraint on the matrix of regression coefficients. This problem is known as reduced-rank regression (RRR) (Izenman, 1975; Reinsel and Velu, 1998; Izenman, 2008) and can be solved via the singular value decomposition. To see this, we write Xϕ−AqX=(Xϕ−AOLSX)+(AOLSX−AqX). [SEP] Justify:

Output: The text does not address the claim about memory recall and instead discusses a mathematical and algorithmic framework related to variance in marginalization and linear regression. There are no references to memory, recall, or stimuli in the text.


Input: Task: corroborate the claim using the given text. Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 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; phenox, phenol oxidase PC1 is associated with Glu (r=0. 895), Cel (r=0. 599), Xyl (r=0. 851) and N-Ag (r=0. 865); PC2 is associated with Pho (r=-0. 799), peptidase (r=0. 650), Lac (r=-0. 695) and Phenox (r=0. 729); PC3 is associated with Sul (r=0. 798), peptidase (r=0. 484) and Perox (r=0. 702). All associations are significant at P<0. 05. Open in new tabDownload slideThe relative abundance of fungi in a given community was significantly associated (P<0. 05) with patterns of enzyme activity. Joint plot vectors in Figure 4 illustrate the strength and direction of significant correlations between fungal communities and enzyme-loading factors at three different levels of community structure. At the ecological function level, the abundance of saprotrophic fungi was strongly related to the patterns of enzyme activity that separated −SB communities from the other two communities along PC1 and PC3 (Figure 4a). This association continued at the higher (Figure 4b, that is, Fungi I. sedis) and lower taxonomic levels (Figure 4c, that is, Mortierella spp. ). By contrast, the enzyme-loading factors along axes PC1 and PC3 that influenced +SB and FH community clustering were generally correlated with the relative abundance of EMF and other mycorrhizal fungi. The abundance of specific EMF genera also strongly correlated with the enzyme-loading factors of PC2 (Figure 4c), which as mentioned above, largely reflects variations in enzymes associated with phosphorus and nitrogen release and lignin degradation. [SEP] Justify:

Output: The text corroborates the claim by discussing the role of fungi, particularly saprotrophic fungi, in enzyme activity that is associated with organic matter decomposition and nutrient cycling. The mention of enzyme activity patterns linked to the relative abundance of fungi suggests that these organisms are integral to the processes of nutrient release and cycling, aligning with the claim.