Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Our field trial provided the first estimates of the size of feral cats living in the dense rainforests and tall wet forests of south-east Tasmania, in areas remote from human settlements. The average estimates of height and length for cats in our study was 25. 3 cm (CI = 24. 4, 26. 1) and 47. 5 cm (CI = 46. 0, 48. 9), which is similar to that of typical domestic cats of 46 cm for length and 23–25 cm for height (Sunquist, 2002). This is particularly true when one considers that our accuracy trial indicated that our methodology consistently over-estimates dimensions. As such, our findings do not contain any evidence that supports the phenomenon of Australian “panthers” and “big cats,” which is commonly reported in the media, but not currently supported by any scientific literature (Menkhorst & Morison, 2012). However, we have only sampled a small pocket of the Tasmanian wilderness herein. The pinhole camera approach we employed provides an opportunity to substantiate claims of giant cats in Australia where shooting and trapping fail or are unavailable, particularly considering this method can be applied to historic data. 5 pinhole camera approach is a cost-effective method to estimate animal body size if using pre-existing camera trap surveys, allowing researchers to exploit past data. There are several caveats associated with our method. A researcher must return to the site of a camera trap survey and replicate deployment to obtain a reference image if using past data, and a wildlife image should only be used if the animal photographed is parallel to the camera-trap, such that the whole flank can be seen. This can limit the amount of data available to be used. Additionally, pixel measurements need to be taken consistently, although this step could be aided by the integration of a measurement AI. Nonetheless, this method provides a non-invasive alternative to live capture or killing that consistently provides precise animal dimensions. We encourage other researchers to test the pinhole approach with other models of camera trap, species, or captive populations to further validate this method. . Paton: Conceptualization (lead); data curation (equal); formal analysis (lead); investigation (lead); methodology (lead); resources (supporting); visualization (lead); writing – original draft (lead); writing – review and editing (equal). Barry W. Brook: Conceptualization (supporting); data curation (equal); supervision (equal). [SEP] Justify:

Output: The text presents evidence of the ongoing use and effectiveness of camera traps, particularly through the pinhole camera method, which is described as a 'cost-effective method to estimate animal body size' and an alternative to traditional live capture methods. This strongly contrasts with the claim by suggesting that traditional wildlife monitoring methods are still viable and potentially beneficial in certain contexts, indicating that eDNA technology may not imminently replace these methods.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2023b) following the global standardised marine taxon reference image database created by Howell et al. , (2019); a total of 582 OTUs were identified in this study. Only one observer annotated the images to reduce the annotation bias. In addition, quality assurance was carried out by re-annotating 5% of the images by the same observer, as recommended in Schoening et al. (2016). A summary of the total number of morphospecies and individuals per site and depth zone are shown in Table S2. The ROV was not equipped with paired lasers during the surveys, thus calculation of the field of view within each image was not possible. To standardise data collection, the pilots positioned the ROV at a similar distance from the seabed, with the same angle of view, for each image collected. The mean time taken to complete a single transect was 30 min 3 s (± 6 min 58 s), with no significant differences observed in the time taken to complete a transect with depth zone and site (Kruskal–Wallis test, p-value > 0. 05. Transect by depth zone: chi-squared = 10. 244, df = 5, p-value = 0. 069; transect by site: chi-squared = 0. 0022733, df = 1, p-value = 0. 962). Thus, given the consistency in the speed of the ROV, it can be assumed a comparable area of seabed was surveyed within each transect and depth zone. To enable comparisons between sites and depth zones, this study focuses on the number of images surveyed and counts of benthic organisms rather than an area-based analysis. Furthermore, as suggested by Colwell et al. (2012) and Roswell et al. (2021), data were standardised by individuals and sampling coverage per depth zone using the iNext package in R software (Hsieh, Ma and Chao, 2020) (see next section). Statistical analysesTo assess the variation in alpha and beta diversity of MCEs over the depth gradient and between the study sites, data were analysed using diversity indices with the iNEXT (iNterpolation/EXTrapolation) (Hsieh, Ma and Chao, 2020) and betapart packages (Baselga and Orme 2012) in R software (Core 2020). [SEP] Justify:

Output: The text does not address the claim regarding coral reefs and their support of diverse marine species. Instead, it focuses on the methodology of a study concerning the identification of organisms and the analysis of diversity indices, without making any specific mention of coral reefs or the diversity of marine species in that context.


Input: Task: corroborate the claim using the given text. Claim: The majority of organic farming avoids the use of synthetic chemicals like pesticides and fertilizers. [SEP] Text: The agricultural community/farmers are struggling to obtain higher rate of yield due to lack of poor knowledge about the soil and water nutrients and suitability of the organic crop for the soil. Most of the farmers use excessive chemical fertilizers in-order to increase productivity of their yield, without aware of side effects. The excess usage of chemical fertilizers by the farmers will have impact on the quality, fertility, and salinity of the soil. To overcome these issues and to promote concept we propose an IoT enabled sensor system for monitoring soil nutrient [NPK] and pH of irrigation water to reduce the manual laboratory method of testing and get the results via mobile application and to promote organic farming in the agricultural field. Smart organic farming based mobile application will further process these nutrients value to predict and suggests the suitable crop to grow and the usage of appropriate amount of fertilizer to maintain the soil fertility there by achieving optimum usage of chemical fertilizer because continuous and wrong usage of these chemical fertilizer have a harmful effect not only on soil but also on crops, we consume leading to unhealthy human life. The proposed mobile application also helps in establishing the connection between farmers and (APMC) in order to avoid fragmentation of profit shares and attain Pricing uncertainty and marketing of the yields by avoiding the middle man. APMC is a state government body which ensures safeguard to the farmers from exploitation by large retailers and suggest the kind of crop to be grown with organic farming. India is well known to produce organic fertilizer which is produced by the waste of slaughterhouses, plant and animal residues, biological products and other natural resources. Thus, the proposed work helps the farmers in adopting stress-free organic farming practice by self-testing their field soil parameters for generating quick soil analysis reports and also helps in connecting with APMC to know the suitable crop for their agriculture land based on the soil and water analysis (SWA) report, dispensing the required amount of organic fertilizer to the soil based on soil and water nutrients analysis using IoT enabled sensor, funding/insurance to the crops in case of occurrence of unpredictable natural disaster in future and direct marketing facility without middle man and maintain sustainable agriculture. In the present era, the industry is at 5. 0 levels but agricultural production is still at 2. 0 levels. In this 1. IntroductionIn the era technology enable modern agriculture, the people are keen about the organic farming due to environmental friendliness and more profitable agriculture. The people are diverting towards the organic farming as it uses less chemical-intensive pesticides. [SEP] Justify:

Output: The text corroborates the claim by discussing the movement towards organic farming which is characterized by 'environmental friendliness' and 'less chemical-intensive pesticides.' It highlights that farmers are increasingly opting for organic farming methods, thus supporting the assertion that organic farming typically avoids synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Abstract Traditionally, spoor (tracks, pug marks) have been used as a cost effective tool to assess the presence and in some cases the individual identity of larger mammals. Automated camera traps are now increasingly utilised to monitor wildlife, primarily as the cost has greatly declined and statistical approaches to data analysis have improved. While camera traps have become ubiquitous, we have little understanding of their effectiveness when compared to traditional approaches using spoor in the field. Here, we 1) test the success of camera traps in recording a range of carnivore species against spoor in realistic field settings (dirt roads in a wilderness reserve); 2) ask if simple measures of spoor size taken by amateur volunteers are likely to allow individual leopards to be tracked in the field and 3) for a trained tracker, ask if this approach may allow individual leopards to be followed with confidence in savannah habitat. We found that camera traps under-recorded mammalian top and meso-carnivores when compared with spoor in the field, with camera traps more likely to underrecord the presence of smaller carnivores (civet 64%; genet 46%, Meller's mongoose 45%) than larger (jackal sp. 30%, brown hyena 22%), while leopard was more likely to be recorded by camera trap (all recorded by camera trap only). We found that amateur trackers could be beneficial in regards to recording leopard presence; however the large variance in measurements of spoor taken by volunteers suggests that this approach is unlikely to allow the collection of further information about individual leopards. Nevertheless, the use of simple spoor measurements in the field by a trained field researcher increases their ability to reliably follow a leopard trail in difficult terrain. This allows researchers to glean further data on leopard behaviour and habitat use without the need for complex spoor analysis. The successful conservation of any species is predicated on our ability to understand its abundance and distribution (Stander 1998, Hussain 2003, Gusset and Burgener 2005, Houser et al. 2009, Trolliet et al. 2014). While some taxa (e. g. birds) have the benefit of being relatively well studied, many species such as the larger mammalian carnivores are notoriously difficult to directly monitor in the field (Stephens et al. 2006). [SEP] Justify:

Output: The text contrasts the claim by discussing the effectiveness of traditional wildlife monitoring methods like spoor (tracks) and camera traps. It highlights that camera traps under-record carnivores compared to traditional methods and stresses that trained field researchers can effectively track leopards using spoor, suggesting that these traditional methods are still valuable. Consequently, the text implies that eDNA technology may not soon universally replace established monitoring methods.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Alpha diversity analysesTo allow comparisons among sites and depth zones, despite possible differences between survey areas covered, a combination of asymptotic and non-asymptotic diversity estimations was applied, based on rarefaction and extrapolation analyses and Hill numbers, using the iNext package in R. Individual-based rarefaction and extrapolation (R/E) curves, as well as sample coverage-based R/E curves, were plotted, with non-overlapping 95% confidence intervals interpreted as a significant difference, as per García-Martínez, et al. , (2017) and Cumming et al. (2007). Individual-based R/E curves were used as they provide unbiased estimates of species diversity and enable comparison of morphospecies diversity between depth zones and sites, specifically when they reach an asymptote. The protocol proposed by Chao et al. (2020) was followed to assess and compare alpha diversity and sample completeness among our surveys. Firstly, Hill numbers, also known as the effective number of equally abundant species qD, were used (Hill 1973; Chao et al. 2014), and the three most widely used orders (i. e. q = 0, q = 1 and q = 2) were assessed here. 0D corresponds to species richness, which is an indication of the number of species present in the community without considering their abundances and thus gives disproportionate weight to rare species. 1D indicates the measure of Shannon diversity (i. e. exponential of Shannon entropy), which weighs each species according to its abundance in the community and can be interpreted as the number of ‘common’ species present in the assemblage. Finally, 2D represents Simpson diversity (i. e. inverse of Simpson concentration index), which discounts all but the dominant species and thus can be interpreted as the number of highly ‘abundant’ (or dominant) species in the community (Chao et al. 2020). To allow for comparisons among the investigated benthic communities, the steps proposed by Chao et al. (2020), were followed as listed below. Firstly, sample completeness of each survey was estimated via a bootstrap method (n = 500) for q = 0, 1, 2 to compare the diversity detected, or sample completeness, between each depth zone and site (Table S3). [SEP] Justify:

Output: The text does not directly address or provide evidence regarding the diversity of marine species supported by coral reefs. Instead, it focuses on methodologies for analyzing species diversity through various statistical measures and does not make a statement about marine species or coral reefs specifically.


Input: Task: corroborate the claim using the given text. Claim: The majority of organic farming avoids the use of synthetic chemicals like pesticides and fertilizers. [SEP] Text: 4. 1 Section 2: Literature surveyOrganic farming started along with the human civilization but it was totally dependent on nature. Due to increase in the population, there is higher demand for the agricultural products also increased. In-order to cope with this demand farmers started using fertilizers. Indiscriminate use of pesticides and chemicals polluted soil, water and whole ecosystem. Off late our farmers from different parts of India are switching over to sustainable agriculture practice in fields by adopting smart farming techniques as part of campaign to promote Digital Agriculture. Exhaustive literature survey is carried out to determine the soil primary nutrients and pH value of the irrigation water of the agriculture field, the technologies employed for the agricultural activities, the mobile applications etc. The research work is enlisted in the Table 2. Sustainability dimensionCriteriaSocial sustainabilityCreating self-reliance in organic productionEmpowering rural communities through partnerships with other farmers to form groups (providing participatory guarantee system and the strengthening of social organization)Increasing farm employmentImproving employment opportunities, particularly in rural areasEconomic sustainabilityImproving efficiency in areas with low inputs (pesticides, herbicides, etc. )Reducing crop damage and Reducing risk of productionSatisfying farmers from an economic perspectiveAdded value of organic products through marketing activities and processingEfficient usage of resources more efficiently (to minimize the use of non-renewable resources)Affordable than traditional agriculture (due to lower variable costs of inputs, identical Fixed costs and higher prices of organic products)Greater economic profitability due to the use of domestic inputsEnhancing the overall performance of the farm in unit areaEnvironmental sustainabilityMaintaining soil fertility in the long-termImproving soil structureIncreasing soil water retention potentialProducing food without chemicalsUsing environmentally friendly production methods (such as the use of animal products, intercropping, use of mulch, using natural pesticides, crop residue returned, green manure, compost, crop rotation)Reducing environmental pollution (pollution reduction of water, soil, and air)Table 1. Important sustainability criteria in organic farming. ReferenceProposed workSensor used[1]IoT sensor for monitoring the soil moisture content with automatic irrigation to save excess water usage. [SEP] Justify:

Output: The text corroborates the claim by stating that organic farming involves producing food without chemicals and utilizing environmentally friendly production methods. It specifically mentions practices like using natural pesticides and reducing environmental pollution, which aligns with the idea that organic farming avoids synthetic chemicals.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Traditionally, indirect methods such as locating den sites, and scat or spoor (tracks, pugmarks) surveys have been utilised as a highly cost effective method (Gusset and Burgener 2005) to determine the presence or absence, abundance or population density for species such as the leopard Panthera pardus, snow leopard Uncia uncia, cougar Felis concolor, lion Panthera leo, caracal Caracal caracal, tiger Panthera tigris and pine marten Martes martes (Beier and Cunningham 1996, Zalewski 1999, Hussain 2003, Melville and Bothma 2006, Sharma et al. 2005, Houser et al. 2009, Sanei et al. 2011, Sheehy et al. 2014). It has been suggested that the sex of tracked Panthera spp. and cougar Felis concolor can be judged from size differ enees in spoor (Bothma 1984, Stander et al. 1997, Stander 1998, Sharma et al. 2003, Sanei et al. 2011, Gu et al. 2014); indeed, if enough features are recorded, leopard, snow leopard, tiger, white rhino Ceratotherium simum and black rhino Diceros bicornis individuals may be identified from spoor alone (Stander et al. 1997, Riordan 1998, Jewell et al. 2001, Sharma et al. 2005, Alibhai et al. 2008, Balme et al. 2009). However, using spoor alone to estimate tiger densities in the field has been criticised for giving unreliable results, as simple measurements or analyses fail to provide the discrimination required (Karanth et al. 2003). Established methods were to take plaster casts or tracings of spoor on acetate from which measurements were taken (Lewison et al. 2001). Sharma et al. (2005) highlighted that data gathered in this way were of poor quality and inconsistently collected. Most feline spoor studies have been investigated in snow (Hayward et al. 2002), tropical (Sanei et al. 2011) or clay substrates (Garcia et al. 2010), sandy loam (Lewison et al. 2001), or thick soil (Sharma et al. 2005), which are often more than 5 mm in depth. Lewison et al. [SEP] Justify:

Output: The text contrasts the claim by discussing the effectiveness and historical reliability of traditional wildlife monitoring methods, particularly the use of spoor and scat surveys, for determining the presence and density of various species. It points out the criticisms regarding the accuracy of these methods, such as the unreliability of estimating tiger densities using spoor alone, thereby suggesting that traditional methods still hold significant value and have not been replaced by eDNA technology.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Secondly, the observed and extrapolated diversities at each site and depth zone were estimated by computing individual-based and sample-size based R/E curves for Hill numbers of q = 0, 1, 2; with extrapolation to a sampling effort equal to double the number of individuals (extrapolation beyond this is unreliable due to prediction bias; (Chao et al. 2014)). Thirdly, the observed and extrapolated Hill numbers were compared to assess undetected diversity and compare diversity between the depth zones and sites (Table S4). However, these results must be taken with caution as they were extracted prior to sample completeness standardisation. Finally, diversity estimates were calculated at a standardised coverage Cmax (e. g. equally complete samples) to statistically compare the benthic diversities along the depth gradient and between sites, which corresponds to the sample coverage of the depth zone exhibiting the lowest coverage when samples are extrapolated to double the number of samples per depth zone at a site (Table S5), as used in El-Khaled et al. (2022). Here, the Cmax value was 96. 4% (Fig. S1; Table S5), corresponding to the lowest sample completeness for q = 0, 1, 2. Beta diversity analysesBeta diversity was measured as the variation in morphospecies with depth and between sites. Jaccard’s dissimilarity was calculated, including its two components, turnover (i. e. species replacement) and nestedness (i. e. subset of a richer community), using the entire benthic community presence-absence data for each depth zone and site. Jaccard dissimilarity varies between 0 and 1, with values close to 1 revealing a high difference in the composition of benthic communities between two depth zones, while a low dissimilarity (close to 0) denotes similar benthic communities. These indices were calculated using the betapart package and plotted in a heatmap, as well as a barplot with ggplot2 package in R (Baselga and Orme 2012; Core 2020). ResultsAlpha diversityA total of 582 distinct morphospecies (OTUs) were identified from the benthic communities at Egmont Atoll, between 15 and 160 m, covering 17 higher taxa (ranging from phylum to genus; Table S2). [SEP] Justify:

Output: The text does not directly address the claim regarding coral reefs supporting a diverse range of marine species. Instead, it focuses on methodologies and metrics for estimating biodiversity in benthic communities, mentioning distinct morphospecies identified but not specifically relating these to coral reefs or their role in supporting marine diversity. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The majority of organic farming avoids the use of synthetic chemicals like pesticides and fertilizers. [SEP] Text: 3248 T/H, since this is the limit of the optimal range for plant nutrients. But when nutrient levels in the soil are below that range, yield naturally begins to decline. Figure 10. (a) The reduction of organic matters first enhances yield largely due to the use of chemical fertilizers, but yield falls over time along with the decline of organic matters; (b) Yield improves with a relatively small uptick in pest population, but when pest population grows significantly, yield drops precipitously; (c) Plant nutrients increase when organic matters fall as a result of the effects of chemical fertilizers, but the plant nutrients actually decline in the end; (d) Increased plant nutrients improve yield within the standard value of plant nutrients; otherwise, production tends to declineFigure 11. State variables converge with different initial valuesState variables, such as yield, organic matters, and plant nutrients, converge over a wide range of initial values, as exemplified by Figure 11. These factors instill confidence in the proposed model's boundedness and the reliability of the study's analysis. As a logical consequence, it may be concluded that the newly proposed model is biologically valid. 4. 12 Results and DiscussionBangladesh is predominantly an agriculture-based country with rice as the major food crop. Furthermore, farming heavily depends on healthy soil. To put it simply, poor soil quality results in subpar yields. It took just ten kilograms of fertilizers to grow five mounds of paddy two years ago, but now it takes between 14 and 15 kilograms to get the same result. Soil fertility has been declining due to the widespread use of chemical fertilizers (Muhammad, 2022). Meanwhile, production is declining. Research in the field of agriculture warns that if the current trend continues, the agricultural system might eventually collapse, causing a serious food shortage in the near future. By analyzing the proposed model for this food security problem, some results can be observed virtually in the form of graphs. Generally, yield is affected by plant nutrients and soil organic matters. Figure 5 suggests that if farmers use more organic fertilizers, it leads to a slow increase in yield and plant nutrients. Commonly used organic fertilizers include composted animal manure, compost, sewage sludge, food processing wastes, and municipal biosolids. They improve soil health and release nutrients to soils gradually. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of organic fertilizers, which are highlighted as alternatives to synthetic chemicals like chemical fertilizers. It mentions that using more organic fertilizers leads to an increase in yield and plant nutrients, which aligns with the principle of organic farming that generally avoids synthetic chemicals for soil health and to improve agricultural sustainability.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: They can have a significant advantage over indirect means of recording individuals, often allowing the population size of cryptic species (notably the large cats) to be estimated using capturerecapture models (Karanth 1995, Karanth and Nichols 1998, Trolle and Kery 2005, Jackson et al. 2006, Balme et al. 2009, Royle et al. 2009, Chapman and Balme 2010, Negroes et al. 2012, Maputla et al. 2013, Tobler and Powell 2013). Although abundance can be estimated relatively accurately when using camera traps (Chapman and Balme 2010) providing that the probability of detection is high, cameras are positioned appropriately and camera avoidance is low (Maputla et al. 2013), there remains a lack of a standardised method of camera trap-based mark—recapture (Kelly 2008) and issues such as camera performance and efficacy have still not been satisfactorily addressed (Maputla et al. 2013, Urlus et al. 2014). However, it is detectability which presents the greatest challenge in effectively sampling the species abundance in a surveyed area (Royle and Nichols 2003) as little is known about how species vary in their likelihood of being recorded by camera traps (Balme et al. 2009, Ballard et al. 2014). In most studies the proportion of individuals which enter the camera trap range and fail to trigger the unit is unknown, resulting in under-estimation of distribution and/ or abundance. One approach is to compare camera trap results with spoor. Lyra-Jorge et al. (2008) found that compared to spoor, film cameras with a trigger delay of one second collectively under recorded by 1. 65 times puma, Puma concolor; maned wolf, Chrysocyon brachyurus; mazama, Mazama sp; striped hog-nosed skunk, Conepatus semistriatus; armadillo, Dasypus sp. and forest rabbit, Sylvilagus brasiliensis. In such studies, spoor is usually recorded in sand traps, which maximises the likelihood of successfully recording a passing individual. In field work such perfect recording opportunities are rare and conditions often affect ability to record spoor (Lyra-Jorge et al. 2008, Balme et al. 2009). [SEP] Justify:

Output: The text contrasts the claim by providing detailed information on the limitations of camera traps and traditional field surveys. It outlines various challenges, such as issues related to detectability and under-estimation of species abundance when using camera traps. The mention that camera traps have a lack of standardised methods and the performance issues associated with them suggests that while eDNA technology may have advantages, traditional methods still face significant challenges that may not necessarily lead to their replacement.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: All morphospecies identified can be consulted in the freely accessible catalogue created for the region (Diaz et al. , 2023b). Species richness (q = 0) was observed to decrease with depth at both sites, with lower diversity observed at 110–120 m and 150–160 m compared to the shallower depth zones (Fig. 2a). Although only conservative minimum estimates could be obtained for species richness (as individual-based R/E curves did not reach an asymptote; Fig. 2a), at the fixed sample coverage of Cmax = 96. 4%, benthic morphospecies richness was significantly lower for the two deepest zones compared to the shallower depth zones (Fig. S1c, Table S5; non-overlapping 95% confidence intervals (CIs) for the two deepest depth zones, overlapping CIs for 15 m to 90 m depth zones). Fig. 2Overview of alpha diversity analysis based on Hill numbers. a) individual-based Rarefaction/Extrapolation (R/E) curves for q = 0. b) individual-based R/E curves for q = 1. c) individual-based R/E curves for q = 2. Shaded areas represent the 95% confidence intervals for each curve. Shades of blue represent the different surveyed depth zones at Rats (IDR), and the shades of orange are the depth zones at (MA). Solid lines are the rarefaction curves while the dashed lines represent the extrapolation curves up to double the respective sample sizeFull size imageEstimates of Shannon and Simpson diversity reached asymptotes for all depths at both sites and overall showed significant decreases in diversity over the depth gradient, from 15 to 160 m (Fig. 2b, c; Fig. S1b, c; non-overlapping CIs). The exception was at IDR, where both Shannon and Simpson diversity were significantly higher at 30–40 m compared to 15–20 m (Fig. 2b, c). At both sites, Shannon diversity between 60–70 m and 150–160 m was significantly lower than observed at shallower depths; however, there was no significant difference in Shannon diversity between 80–90 m and 110–120 m (Fig. 2b). Simpson diversity differed between the two sites (Fig. [SEP] Justify:

Output: The text provides detailed analysis regarding species richness and diversity across different depths in a marine environment but does not explicitly support or refute the claim about coral reefs and their role in supporting a diverse range of marine species. It discusses observations of decreasing diversity with depth, but it lacks relevant information that directly connects to the claim about coral reefs.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: Abstract Experience-dependent modifications of synaptic connections are thought to change patterns of network activities and stimulus tuning with learning. However, only a few studies explored how synaptic plasticity shapes the response dynamics of cortical circuits. Here, we investigated the mechanism underlying sharpening of both stimulus selectivity and response dynamics with familiarity observed in monkey inferotemporal cortex. Broadening the distribution of activities and stronger oscillations in the response dynamics after learning provide evidence for synaptic plasticity in recurrent connections modifying the strength of positive feedback. Its interplay with slow negative feedback via firing rate adaptation is critical in sharpening response dynamics. Analysis of changes in temporal patterns also enables us to disentangle recurrent and feedforward synaptic plasticity and provides a measure for the strengths of recurrent synaptic plasticity. Overall, this work highlights the importance of analyzing changes in dynamics as well as network patterns to further reveal the mechanisms of visual learning. Introduction Experience-dependent changes in neural responses have been suggested to underlie the more efficient and rapid processing of stimuli with learning. Human and monkeys have been reported to process familiar stimuli with shorter response times and with less effort (Greene and Rayner, 2001; Logothetis et al. , 1995; Mruczek and Sheinberg, 2007). The possible neural correlate for such behavior enhancement is the sharpening of stimulus selectivity that is achieved by broadening the distribution of activities as the stimulus becomes familiar (Freedman et al. , 2006; Kobatake et al. , 1998; Lim et al. , 2015; McKee et al. , 2013; Woloszyn and Sheinberg, 2012). Also, temporal sharpening of neural responses with experience has been observed, which can increase the resolution of discriminating stimuli in time with learning (Meyer et al. , 2014; Recanzone et al. , 1992). Modifications of synaptic connections have been thought to be one of the basic mechanisms for learning. A repeated encounter of a stimulus would elicit a particular activity pattern in the network, which in turn modifies synaptic connections depending on pre- and post-synaptic activities. Such modifications of synaptic connections lead to changes in neural responses that can be a substrate to differentiate learned and unlearned stimuli. [SEP] Justify:

Output: The text corroborates the claim by stating that modifications of synaptic connections are thought to be one of the basic mechanisms for learning. It discusses how repeated encounters with stimuli elicit specific activity patterns that modify synaptic connections based on pre- and post-synaptic activities, thus supporting the idea of synaptic plasticity being central to memory formation.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Camera traps work was part of a wider study of the ecology of leopards, utilising a network of over 30 camera traps across an area of 5400 ha. Camera traps were sited along unpaved dirt roads, which tend to be used as highways by many animals (Rowcliffe and Carbone 2008). Four sites (Fig. 1) were chosen among these to 1) maximise the likelihood of recording high numbers of predatory mammals, 2) have the correct natural substrate for spoor recording and 3) were within walking distance of base camp in order to collect data before the movement of vehicles occurred. The cameras 5210A camera traps were utilised in this study. Each camera unit had a trigger time of one second and was set to photo mode, at normal sensitivity level. The cameras have three sensors, with two requiring activation before an image is taken. The sensors detect motion and heat and can be triggered up to 15 m away. Images were taken in bursts of three and the interval between captures was set at 30 s to reduce battery depletion resulting from moving vegetation or large groups of animals passing by the site. A single camera was placed at one side of a ‘T’ junction at four study sites, to increase the probability of capturing an animal. The camera unit was attached to a metal stake which was pushed into the ground resulting in the lens being positioned approximately 45 cm off the ground with the sensors at 40 cm. This height allowed for a range of species including meso-carnivores to trigger the sensors. The camera was angled 45° to the road to allow an animal to be in range of the lens during the one second delay between the sensors being triggered and the animal being photographed. Any vegetation which could trigger the camera or hinder the view of an animal in the image was removed prior to the survey and during monitoring. Camera sites were checked every morning, with cards and batteries being exchanged once a week. As the sites were part of a longer running study in a commercially utilised part of the reserve we felt that our activity would not impact greatly on the normal movement of animals living in the area. However to check for this we compared spoor located inside and outside of the trap zone as discussed under spoor surveys. Additionally, we compared image capture rate in weeks preceding and following the study, and found no difference in image collection rates (data not shown). [SEP] Justify:

Output: The text contrasts the claim by providing a detailed account of the use of camera traps for wildlife monitoring. It describes how camera traps are effectively used, detailing their setup and functionality, and implies that traditional methods like camera trapping are still integral to ecological studies, thus challenging the assertion that eDNA technology will soon replace these conventional methods.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2c): at MA, Simpson diversity was significantly different between 15–20 m and 30–40 m and the deeper depths; however, the values did not differ significantly between the depth zones from 60–70 m to 110–120 m; while at IDR, the 80–90 m depth zone showed a significantly lower Simpson diversity, compared to both 60–70 and 110–120 m (Table S5). Finally, the values were significantly lower at 150–160 m compared to shallower depth zones for both sites (Fig. 2c; Fig. S1b, c; Table S5). In this study, the diversity curves did not all reach an asymptote, indicating that total diversity was not captured (Fig. 2). This undetected diversity for each depth zone at each site is detailed in Table S4. Nevertheless, coverage-based sample completeness profiles revealed that between 93. 8% and 97. 3% of benthic morphospecies diversity was detected, depending on the depth zones and sites (Table S3), showing that while a few species were undetected, an acceptable standardized value for comparing species richness between communities was sampled (Moreno and Halffter 2000). Beta diversityValues of beta diversity between depth zones for the entire benthic community were similar between both study sites (Fig. 3a and d). Total beta diversity values were high between the depth zones, throughout the entire depth gradient for both sites (from 15 to 160 m; Jaccard 0. 61 to > 0. 96; Fig. 3, Table S6, Fig. S1), indicating a limited number of shared morphospecies between depth zones. Adjacent depth zones displayed less dissimilarity compared to depths located further apart (Jaccard 0. 65 for 15–20 m/30–40 m, to 0. 81 for 110–120 m/150–160 m; 0. 85 for 80–90 m/150–160 m to 0. 95 for 15–20 m/150–160 m, respectively; Fig. 3a and d, Table S7, Fig. S1). Fig. 3Changes in beta diversity and its two components–turnover and nestedness – with depth for the entire benthic community at both study sites Rats (IDR, a to c) and (MA, d to f), using Jaccard’s dissimilarity index from 0 to 1. [SEP] Justify:

Output: The text does not directly address the claim about coral reefs supporting a diverse range of marine species. Instead, it discusses the diversity of benthic morphospecies at different depths, but does not specifically mention coral reefs or their biodiversity. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: The previous modeling works investigated the relationship between synaptic plasticity and changes in network activity to find a synaptic plasticity rule that can account for sharpening of stimulus selectivity observed with learning (Dayan and Abbott, 2005; Gerstner and Kistler, 2002). However, whether such rules can also explain temporal changes in neural responses is in question. In this work, we investigate the mechanism underlying changes of response dynamics with learning. To this end, we consider neural activities recorded in inferior temporal cortex (ITC) known to be important for visual object recognition (Miyashita, 1993; Tanaka, 1996). In ITC, changes in the response properties with learning have been reported in several experiments (Freedman et al. , 2006; Li et al. , 1993; Lim et al. , 2015; Logothetis et al. , 1995; McKee et al. , 2013; Woloszyn and Sheinberg, 2012; Xiang and Brown, 1998). The average over different visual stimuli of time-averaged responses decreases with familiarity, while the distribution of responses across visual stimuli broadens with learning. The dynamics of visual responses were also found to change with familiarity – in particular, rapid successive presentation of familiar images, but not novel images, elicits strong periodic responses (Meyer et al. , 2014). Previously, we investigated synaptic plasticity in recurrently connected circuits that reproduces changes in the distribution of time-averaged visual responses observed experimentally (Lim et al. , 2015). As the distribution of time-averaged visual responses of a single cell to multiple stimuli can be a surrogate for a spatial pattern of the network to one stimulus in a homogeneous network, the previous work mainly focused on how recurrent synaptic plasticity shapes the network pattern and stimulus tuning. Here, we extend our previous framework to understand mechanisms underlying changes of temporal patterns with learning. First, we demonstrate that the synaptic plasticity rule inferred from the time-averaged responses is not sufficient to reproduce changes in response dynamics. Next, we show that the interaction between synaptic plasticity and negative feedback mechanisms is critical for generating stronger oscillation after learning. Using a mean-field analysis, we identify the conditions on synaptic plasticity and negative feedback to reproduce changes in response dynamics consistently observed in different experimental settings. Finally, we validate these conditions through network simulations and infer the post-synaptic dependence of synaptic plasticity from the experimental data. [SEP] Justify:

Output: The text corroborates the claim by discussing the investigation of synaptic plasticity and its relationship with neural response dynamics during learning. It explains how synaptic plasticity rules are explored in the context of changing network activity and response properties associated with learning, demonstrating that it plays a crucial role in how memory is formed through the strengthening of neural connections.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: The thickness of substrate was recorded as being below 2 mm or over 2 mm (rarely more than 5 mm). Only complete and clear spoor were recorded. Partial prints were not recorded to avoid misidentification. Species identification from spoor recorded in the field were checked by observers using Liebenberg (2005) and re-checked from the image by an experienced tracker. For both camera trap and spoor surveys, presence of a given species was logged once per night per location, irrespective of the number of spoor and/or images recorded. Figure 2Open in figure viewerPowerimage of carnivore spoor taken for confirmation of species identification with measurement zeroed at the back of the metatarsal or metacarpal. Inter-observer variation in spoor measurements A plaster cast of a clear leopard print was made from a spoor found in fine mudstone sand over 2 mm thick. Hairspray was used to fix the track to avoid the p13. 3, p = 0. 02; Table 1) and that there was no camera avoidance. Table 1. Number of spoor recorded inside and outside of the camera trap area for each species during the study. Spoor found inside the trap area Spoor found outside the trap area Hyaena brunnea 15 10 Canis s2. Number of recordings per species during the study period as camera trap images alone, spoor alone, or both images and spoor. images recorded Only spoor recorded Both spoor and image Hyaena brunnea 4 8 3 Canis s0. 114 total recordings of species camera-1 h-1. For spoor in the trap area, a mean of 0. 25 species trap area-1 h-1 was recorded, suggesting that overall spoor recorded the presence of a species more than twice as frequently as the camera traps. There was no significant difference between the number of nights species were recorded using the camera traps or using spoor located within the trapping area when leopard was included (t5 = 2. 57, p = 0. 23; Table 2). However when leopard was omitted from the analysis the result became highly significant (t4 = 2. 78, p < 0. 01), indicating camera traps significantly under-recorded compared to spoor. Leopard was recorded on three occasions by camera trap during the study, but not by spoor inside the trap area. [SEP] Justify:

Output: The text contrasts the claim by detailing findings from a study that analyzed the effectiveness of traditional wildlife monitoring methods, highlighting that spoor (tracks) recorded species presence more frequently than camera traps. It suggests that traditional methods are still relevant and effective, thereby opposing the notion that eDNA technology will completely replace them.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Values close to 0 indicate a high level of similarity between the two communities being compared and values close to 1 indicate a low level of similarityFull size imageTurnover was the largest component of beta diversity, thus it can be considered as the main driver of differentiation between benthic communities, indicating that the change in morphospecies diversity along the depth gradient, from 15 to 160 m, was mostly due to species replacement (Jaccard > 0. 5 to > 0. 9; Fig. 3b and e; Table S7, Fig. S1). On the contrary, nestedness had only a limited influence on the overall dissimilarity with depth (Jaccard 0. 01 to < 0. 2; Fig. 3c and f; Table S7, Fig. S1); showing that, while species diversity broadly decreased with depth (Fig. 2), deeper communities were not just a subset of richer communities observed at shallower depths. The differences between the turnover and nestedness components, compared to total dissimilarity, are detailed in Fig. S1. Between sites, for the same depth zone, beta diversity did not show a strong pattern (Fig. 4). Overall, dissimilarity was less pronounced between sites in shallow-water communities (15–20 m) compared to communities at mesophotic depths (30–160 m), with 60–70 m and 150–160 m zones showing the highest benthic dissimilarity at a local scale. Similarly to the beta diversity calculated along the depth gradient, turnover was the main component of beta dissimilarity between sites, indicating benthic dissimilarity is mainly driven by species replacement at a local scale. Fig. 4Jaccard’s dissimilarity and its two components – turnover and nestedness – between the two study sites (Ile des Rats and Manta Alley) for a depth zone. Jaccard’s dissimilarity index ranges from 0 to 1, with values close to 0 indicating a high level of similarity between the two communities and values close to 1 indicating a low level of similarityFull size imageDiscussionThis study provides novel, baseline data on the diversity of benthic communities within MCEs in the central (Egmont Atoll, Chagos Archipelago). [SEP] Justify:

Output: The text does not address the claim directly; instead, it focuses on the concepts of beta diversity and dissimilarity between benthic communities across different depths, without specifically mentioning coral reefs or the diversity of marine species. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: In sum, the prominent effects of visual learning on responses of excitatory neurons are (i) reduction in average response, (ii) increase in maximum response, and (iii) stronger oscillations after learning. In the following, we show how such changes guide us to reveal a mechanism underlying visual learning that sharpens stimulus selectivity and temporal resolution of stimuli. Note that we focus on excitatory neurons only assuming that the dynamics of inhibitory neurons follow that of mean excitatory neurons, and do not contribute qualitative changes of response dynamics after learning. Such a simplification is based on the experimental observation that input changes and the magnitude of rebound activity depend weakly on the post-synaptic firing rates in inhibitory neurons (for further justification). Recurrent synaptic plasticity alone cannot reproduce the response dynamics Activity-dependent modifications of synaptic connections can be one of the key elements to explain changes in network patterns and response dynamics with learning. Previously, we introduced a procedure to infer synaptic plasticity rules from experimental data so that networks implementing the derived learning rules can quantitatively reproduce changes in the distribution of time-averaged visual responses observed experimentally (Lim et al. , 2015). We now extend this framework and explore whether synaptic plasticity alone would be sufficient to explain stronger oscillatory responses after learning. To investigate the effect of learning on response dynamics, we considered a firing rate model with a plasticity rule that modifies the strength of recurrent synapses as a function of the firing rates of pre- and postsynaptic neurons. Activities of neurons are described by their firing rates ri for i = 1,…, N, where N denotes the number of neurons in the network. Their dynamics are described by the following equations (1) τrdridt=−ri+Φ(∑WijRrj+∑WijFIjX) where Φ is a static transfer function (f-I curve), and the total input current is the sum of the recurrent input ∑WijRrj and the feedforward input ∑WijFIjX. Wijk denotes the strength of synaptic connection from neuron j to neuron i with k = R or F representing recurrent and feedforward connections, respectively. The superscript X denotes an external input, and IiX is the external input to neuron i before learning with WijF=δij. [SEP] Justify:

Output: The text corroborates the claim by discussing how activity-dependent modifications of synaptic connections are key to explaining changes associated with learning. Specifically, it mentions that recurrent synaptic plasticity and the modification of synaptic strength in response to neuronal firing rates are fundamental to understanding response dynamics after learning, which aligns with the concept of synaptic plasticity being involved in memory formation.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: These were identified as study individuals MS24, MS24 and FS7. There is a significant correlation between carnivore species recorded by both methods (Rs = 0. 81, n = 6, p < 0. 05;Fig. 4) indicating that an increase in activity is detected by both methods. Each species' mass and length (including tail where this is visible) were taken as the mean values for the male and female of each species (from Kingdon 1997 and Skinner and Chimimba 2005). The percentage difference in recording between camera traps and spoor was found to be nonsignificant, but there was a borderline trend with length (one-tailed Pearson r = -0. 724, n = 6, p = 0. 052; Fig. 5), suggesting that relatively short species such as the genet or Meilers mongoose may be more likely to be under-recorded than longer species like hyena and leopard. Inter-observer variation Of the 20 prints made, 19 were useable and in one print, measurement D was not possible due to damage during recordings. There was a highly significant difference between observers for each measurement (Table 3). The experienced researcher had a greatly reduced variation between measurements compared to the three novices. Intra-observer variation Hind right measurements were omitted from the analysis due to only one animal registering in substrate < 2 mm and two animals only registering in substrate > 2 mm. With the exception of hind pad width, there was a highly significant difference in all measurements between the animals and a significant difference was found between the two substrates for all measurements (Table 4) suggesting that substrate depth affects spoor size. The full spoor length was found to be the most distinct between each animal, supporting results of Sanei et al. (2011), followed by hind spoor length, with full spoor width only showing a difference in substrate > 2 mm (Table 5). ability to document the presence (and potentially abundance) of species of conservation concern is of great interest to field ecologists (Karanth and Nichols 1998, Trolle and Kery 2005, Jackson et al. 2006). Understanding the limitations of the main means of recording these species is therefore of considerable importance. In this study of mammalian carnivores inhabiting savannah habitat in South Africa, we found that camera traps significantly under-recorded the number of animals passing a trapping area when compared with those identified using spoor. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that traditional methods, specifically camera traps, significantly under-record animals compared to other techniques like spoor tracking, indicating that these traditional methods still have substantial limitations. The statement suggests there is a need to understand the limitations of traditional methods, such as those posed by camera traps, and that they are not being completely replaced yet, but rather evaluated for their effectiveness in documenting wildlife presence.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: 2023a), gorgonian and antipatharian (Bridge et al. 2012; Bo 2019; Rowley et al. 2019; Diaz et al. 2023a)) diversity in MCEs were shown to increase with depth. Similar trends in specific phylum change are presented in Table S2. Benthic diversity and community composition change along the depth gradient may be explained by several environmental factors, as observed in a parallel study by (Diaz, et al. 2023a), which identified fifteen environmental variables contributing significantly to the distribution and community composition of MCEs over the depth gradient. Decreasing photosynthetically active radiation (PAR) and temperature were identified as the two main drivers of variation in community composition with depth at (Diaz et al, 2023a). Similarly, a global study across a variety of taxa identified PAR and nutrient availability (both dissolved and particulate organic matter) to be correlated with a community break observed at 60 m (Lesser et al. 2019). In the Caribbean (i. e. Bermuda and Little Cayman), temperature and light were the main factors correlated with changes in overall benthic morphotype richness and scleractinian corals with depth, respectively (Stefanoudis et al. 2019; Carpenter et al. 2022). A decrease in benthic diversity with depth can therefore be explained by several factors, but mostly by the decrease in PAR, temperature, and nutrient availability. These factors are important drivers of primary production, reflected here in the distribution of photosynthetic corals and algae. Nevertheless, while morphospecies richness decreased with depth overall for the two study sites; for a standardised sample coverage (i. e. Cmax) and within the four shallowest depth zones (from 15 to 90 m), this decrease was not significant (Fig. 2d). However, Shannon and Simpson indices (i. e. , accounting for abundant and highly abundant morphospecies), revealed significant differences between depth zones with an overall decrease in diversity indices with depth (except between 80–90 m and 110–120 m for q = 1, Fig. 2e; and between 60–70 m and 110–120 m at IDR, 80–90 m and 110–120 m at MA for q = 2, Fig. 2f). [SEP] Justify:

Output: The text discusses various environmental factors that affect benthic diversity and community composition in marine ecosystems, particularly with depth. However, it does not explicitly mention coral reefs or their role in supporting diverse marine species. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: We assumed that the recurrent synapses are plastic, changing their strengths according to WijR→WijR+ΔWijR, which depends on the activities of both pre- and postsynaptic neurons during the stimulus presentation. We further assumed that the learning rule is a separable function of pre- and postsynaptic activity as (2) ΔWijR=1NfR(ξi)gR(ξj) where f and g are post- and pre-synaptic dependence of the learning rules, respectively, and ξi is the activity of neuron i averaged during the stimulus presentation before learning. Previously, we found that synaptic plasticity in recurrent excitatory connections is sufficient to reproduce changes in the distribution of time-averaged visual responses observed experimentally (Lim et al. , 2015). Hebbian-type synaptic plasticity with a potentiation in high firing rates leads to an increase of the maximal response of excitatory neurons, while overall depression leads to a decrease of the average network response of both excitatory and inhibitory neurons (Figure 2A). With such synaptic plasticity derived from the time-averaged activities, response dynamics in Equation (1) shows similar changes to the time-averaged responses (Figure 2B,C). However, the temporal profile is similar before and after learning and does not show oscillations after learning. Thus, synaptic plasticity alone is not sufficient for reproducing changes in response dynamics observed experimentally, which will be shown analytically in the next section. Figure 2 Download asset Open asset Networks with synaptic plasticity in recurrent connections without slow negative feedback. (A) Example post-synaptic dependence of recurrent synaptic plasticity inferred from changes of time-averaged responses. Dependence of synaptic plasticity on the post-synaptic rate, f in Equation (2), shows depression for low rates and potentiation at high rates. (B, C) Average (B) and maximal (C) response before (red) and after (blue) learning for the network with synaptic plasticity only in recurrent connections. Interactions between recurrent synaptic plasticity and slow negative feedback Another key ingredient to explain changes in response dynamics with learning can be slow negative feedback. In a dynamical system, resonance-like behavior emerges from the interaction between strong positive feedback and relatively slow negative feedback. [SEP] Justify:

Output: The text corroborates the claim by discussing how recurrent synapses exhibit plasticity that changes their strengths based on the activity of pre- and postsynaptic neurons. This directly supports the concept of synaptic plasticity as a mechanism for memory formation, particularly when it states that synaptic plasticity is sufficient to reproduce certain changes in visual responses, which aligns with the notion of strengthening neural connections. The detailed explanation of how synaptic plasticity operates under high firing rates and influences the responses of neurons further underscores its role in memory processes.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: This under-recording ranged from 22% for brown hyena to 64% for civet and there is a suggestion that animal size affected the likelihood of being recorded. This study illustrates that spoor can provide us with an opportunity to calibrate camera traps. However, the ability to detect and/or identify spoor is affected by the tracker's expertise as well as ground characteristics (Alibhai et al. 2008, Lyra-Jorge et al. 2008) which do need to be taken into consideration, but could be controlled where necessary. We found that inter-observer variation in the simple measurements recorded in our study meant that spoor size data collected by amateur volunteers are unlikely to provide useful data beyond the presence/absence of leopards. However, experienced researchers were more consistent in measuring spoor, although spoor size was influenced by substrate, as well as other factors such as terrain and speed of the animal's movement (Riordan 1998, Liebenberg 2005, Sanei et al. 2011). We found that mean leopard spoor size was significantly larger in substrate with a depth of > 2 mm when compared with those recorded in < 2 mm, which may result from the greater displacement of the toes from the metatarsal pad as the foot pushes further into the substrate. Full spoor length was significantly different between animals, which was more apparent in substrates > 2 mm depth, although we advise that measurements taken directly in field are best done in < 2 mm sandy substrate as this reduces variance. This is in contrast to Lewison et al. (2001) and Sharma et al. (2005), who found thicker soil to be more beneficial for tracings and photographs, especially for digital measurements. Therefore, while simple spoor measurements collected directly in the field in sandy soil may not reliably allow the identification of individuals, simple spoor measurements of the leopard of interest can be taken, so that where the trail is broken or crossed by another individual, size measurements provide evidence that the focal leopard's trail has been relocated. Figure 4Open in figure viewerPowerof images of each study species recorded inside the camera trap area by spoor or camera trap images. Figure 5Open in figure viewerPowerbetween study species body length (cm) and likelihood of being under-recorded by camera trap (considering the percentage difference between spoor and image records) in this study. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and variables affecting traditional wildlife monitoring methods like camera traps and spoor measurements. It highlights how these methods, especially camera traps, provide essential data on wildlife presence and behavior, emphasizing the complexity and expertise required in traditional methods, which suggests they will not be easily replaced by eDNA technology. Furthermore, it notes the benefits of calibration and measurement variability even within traditional monitoring, implying that these methods still have significant value.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Alpha and beta diversity indices were assessed through the visual annotation of underwater images, where all organisms were identified to the highest taxonomic resolution possible and distinct morphospecies were assigned an Unit (OTU), as standard taxonomic approaches are not often applicable to image data. A morphospecies may then correspond to species, genus, family or higher taxonomic levels. Whilst images were annotated by the same observer (who also re-annotated 5% of the images for quality assurance), this method lacks taxonomic resolution, which may consequently have had a negative effect on the diversity results presented here and led to the grouping of several species within one morphospecies, or on the contrary, separation of one species into several colour morphs. During the current study, it was not possible to collect the physical samples typically required to enable traditional taxonomic investigation. Taxon differentiation was thus missing at the fine scale, molecular and genetic levels, likely leading to lower (or higher) observed diversity values and differences in diversity than actually occurs in reality. MCEs are still relatively poorly understood in taxonomic terms, thus, high-resolution images with close-ups in addition to physical sample collection and taxonomic expertise are crucial to resolve their true diversity and the variation in taxa along the depth gradient. This study investigated alpha and beta diversities for benthic communities, along a large depth gradient, for the first time in the central Indian Ocean. These indices are crucial indicators to provide evidence-based information for management and conservation actions. Indeed, despite meeting the international criteria used to identify priority areas for conservation policies (Soares et al. 2020), MCEs are still considered under-protected and under-represented in marine spatial planning (Stefanoudis et al. 2022), and they are usually incidentally included in MPAs due to geopolitical boundaries, rather than purposefully (Turner et al. 2019; Stefanoudis et al. 2022). MCEs, while encompassed within the fully protected Chagos MPA since 2010, have not been included in research and specific protection until 2018, where an objective to improve their research and protection by 2023 was mentioned in the conservation management planning workshop (Programme 2018). However, the authors are not aware of any specific management steps being taken regarding the protection of MCEs within the Chagos MPA, other than the research presented here. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the methods of assessing biodiversity in marine ecosystems, particularly focusing on methodological challenges in taxonomic identification, without directly addressing or providing evidence related to the diversity of marine species supported by coral reefs.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: Thus, enhanced resonance behavior after learning observed experimentally may suggest that changes in synaptic connections strengthen positive feedback in the circuit and affect the response dynamics by interacting with a slow negative feedback mechanism. Also, the reduced response to successive stimulus presentation of novel stimuli (Meyer et al. , 2014) can be caused by the slow recovery from negative feedback. For generating a damped oscillatory response after learning, we found that specific negative feedback such as firing rate adaptation is required (Figure 3). Similar to previous works investigating the effect of adaptation on the network activity in a mean-field approach (Fuhrmann et al. , 2002; Laing and Chow, 2002; Tabak et al. , 2006; Treves, 1993; van Vreeswijk and Hansel, 2001), we considered a linear mechanism for adaptation where the adaptation current is a low-pass filtered firing rate represented by the variable ai with time constant τa and strength k. Then the dynamics of network activity is described by the following equations: (3) τrdridt=−ri+Φ(∑ WijRrj−kai+∑ WijFIjX) τadaidt=−ai+ri Figure 3 with 2 supplements see all Download asset Open asset Mechanism of generating damped oscillations after learning. (A) Schematics of the dynamics after learning. The overlap variable m is similar to activities of high rate neurons represented as r1, and n represents adaptation in m. These high rate neurons drive a damped oscillation in the remaining population whose strength is proportional to the post-synaptic dependence of recurrent synaptic plasticity fR(ξi). (B) Interactions between potentiation of recurrent inputs and a slow adaptation mechanism. The strength of potentiation is proportional to fg¯R (Equation 4) which is 0 before learning. The separatrix dividing overshoot and damped oscillations is shown as a parabola defined by fg¯R, the strength of adaptation k and time constants τR and τA. Intuitively, interactions between recurrent synaptic plasticity and adaptation-like negative feedback in Equations (2) and (3) can reproduce two effects of visual learning, increase in maximal response and stronger oscillatory response after learning. [SEP] Justify:

Output: The text corroborates the claim by discussing how changes in synaptic connections, particularly through mechanisms like recurrent synaptic plasticity, enhance circuit activity and create stronger response dynamics after learning. It highlights that interactions between synaptic plasticity and adaptations can result in increased response and strengthened oscillatory behavior, which directly relates to the concept of memory formation as a function of synaptic plasticity.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: 28 (DF = 3) p < 0. 001 Table 4. values for each spoor measurement (mm; Fig. 3) for three leopard hind left spoor sets taken in the field. None of the interaction terms was significant. DF F p Full spoor length (A) animal 2, 48 37. 06 0. 001 substrate 1, 48 5. 36 0. 025 Full spoor width (B) animal 2, 51 5. 06 0. 010 substrate 1, 51 13. 50 0. 001 Hind pad length (C) animal 2, 49 17. 51 0. 001 substrate 1, 49 9. 37 0. 004 Hind pad width(D) animal 2, 49 1. 46 0. 244 substrate 1, 49 4. 68 0. 036 In contrast, camera traps readily allow identification of species and individuals when variable pelage markings are evident, and often their general size or sex can be determined. Increasing the likelihood of detecting focal species is extremely important to avoid under-recording (Maputla et al. 2013). Our work suggests that this could be a more significant factor to consider when utilising camera traps than perhaps previously thought. Trolle and Kery (2005) found dirt roads to be more effective than game trails in capturing carnivores on camera, so we are likely to have maximised our capture rate, but evidently many individuals were not recorded by the camera traps. While the results for leopard are based on a small sample, results from our long term study (Pirie, Thomas and Fellowes unpubl. ) also suggest camera traps record the presence of this species approximately twice as often as spoor. All four sites were picked partly because of the ideal substrate conditions, although these can alter between days due to changing temperatures, humidity, rainfall and wind strength (Alibhai et al. 2008, Lyra-Jorge et al. 2008). On two occasions, leopard spoor was registered outside of the camera trap zone and the leopard was photographed by the camera trap, but no complete leopard spore was detected within the camera trap zone. This is likely due to slight variations in the substrate within the camera trap zone. Partial spoor was not recorded to avoid misidentification, so was not recorded as being inside the trap zone. Overall, under-recording appears to occur more frequently with smaller species. Previous studies (Kelly 2008, Lyra-Jorge et al. [SEP] Justify:

Output: The text contrasts the claim by discussing the effectiveness of camera traps in wildlife monitoring and suggesting that they may outperform methods like spoor tracking. It emphasizes the advantages of camera traps in accurately identifying species and individuals, noting that they record the presence of leopards more often than spoor, which suggests traditional methods may still be highly valuable in monitoring wildlife.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: AbstractLow-altitude high-resolution aerial photographs allow for the reconstruction of structural properties of shallow coral reefs and the quantification of their topographic complexity. This study shows the scope and limitations of two-media (air/water) Structure from Motion—Multi-reconstruction method using drone aerial photographs to reconstruct coral height. We apply this method in nine different sites covering a total area of about 7000 m2, and we examine the suitability of the method to obtain topographic complexity estimates (i. e. , seafloor rugosity). A simple refraction correction and survey design allowed reaching a root mean square error of 0. 1 m for the generated digital models of the seafloor (without the refraction correction the root mean square error was 0. 2 m). We find that the complexity of the seafloor extracted from the drone digital models is slightly underestimated compared to the one measured with a traditional in situ survey method. Similar content being viewed by others Detecting change in coral reef 3D structure using underwater photogrammetry: critical issues and performance metrics 2. 5D) mapping of very shallow (< 3 m depth) coral reefs is a challenge. Large corals reach up to mean sea level, thus preventing the navigation of vessels carrying ship-borne echo sounders and limiting divers’ access to continuous top-view data. Airborne bathymetric active light detection and ranging (LiDAR) can overcome these limitations and provide continuous sub-meter resolution data with a vertical accuracy in the range of 1–2 dm for shallow-water coral reefs (Collin et al. 2018). surveys are costly and likely unjustified for small areas, and they allow for minimal flexibility in the survey design. In the last years, System (UAS, also called drones) started to be regarded as a third-generation source of remote sensing data (et al. 2018), providing scientists new and accessible tools to explore the Earth surface (e. g. , Casella et al. 2020; Dugdale et al. 2019; Castellanos-Galindo et al. 2019; Mlambo et al. 2017) and allowing significant scientific advancement (e. g. , Ramanathan et al. 2007). UAS regulation that has hindered early scientific uses of UASs (Coops et al. 2019) is now moving forward. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the methods and challenges of surveying and reconstructing the structural properties of coral reefs without mentioning the diversity of marine species supported by these ecosystems.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: Hebbian-type synaptic plasticity in recurrent connections provides strong potentiation in the connections among high firing rate neurons, and thus, generates a cell assembly with stronger positive feedback after learning (Figure 3A). This leads to not only an increase in the response of this cell assembly but also the emergence of oscillation under the interplay with slow adaptation currents. The strength of oscillation in the rest of the population may depend on the synaptic strengths from these high firing rate neurons. To show this analytically, we investigated mean-field dynamics that summarize network activity with fewer variables (Materials and methods). To facilitate the analysis, we made two assumptions, linear dynamics with transfer function Φ(x) = x, and homogeneous connectivity before learning that reflects no correlation between novel stimuli and network structure. Under these assumptions, the dynamics before learning is described by average activity and adaptation, r¯=1N∑iri and a¯=1N∑iai. After learning, with synaptic plasticity in recurrent connections following Equation (2), recurrent connections become correlated with activity pattern they learned. Increased correlation between the learned pattern and network structure can be captured by additional variables m and n, defined as m=1N∑igR(ξi)ri and n=1N∑igR(ξi)ai, which is a variation of the pattern overlap 1N∑iξiri utilized previously to describe changes in dynamics with learning (Tsodyks and Feigel'man, 1988). The variables m and n can approximately represent the activities and adaptation of high firing rate neurons as the activities and adaptation of high firing rate neurons contribute more to m and n variables with monotonically increasing pre-synaptic dependence gR(ξi) (Figure 3A). Thus, potentiation of recurrent inputs in high firing rate neurons provides strong positive feedback in m, while slow adaptation mechanisms represented by n variables provide negative feedback. As the variables m and n are only present in the dynamics after learning, qualitative changes of the response dynamics in the network should be mainly led by their dynamics with strong potentiation in high rate neurons (Figure 3B). Such strong potentiation and generation of damped oscillation in high rate neurons are consistent with the observation that the rebound is strongest in those neurons (Figure 1D). [SEP] Justify:

Output: The text corroborates the claim by detailing how Hebbian-type synaptic plasticity in recurrent connections enhances the connections among high firing rate neurons after learning. It discusses the generation of a cell assembly with stronger positive feedback, indicating the strengthening of neural connections, which aligns with the definition of synaptic plasticity as it relates to memory formation.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: 2008, but see Negroes et al. 2012) support this view. In contrast, Urlus et al. (2014) found that larger Australian mammals were more likely to be under-recorded by camera traps. It is likely that this is as a result of the much greater size difference in their study, with smaller mammals having a more restricted home range around the traps, and their use of bait stations to attract smaller species (Urlus et al. 2014). Trigger time, the interval between sensors detecting movement and an image being taken, may also play a role in under recording given that small animals may not trigger the sensors or move more rapidly out of the capture zone once the sensors have been activated. Negroes et al. (2012) found having two cameras per station was on average 29% more effective than a single camera based on photographic rate. In addition, camera traps can vary greatly in their likelihood of capturing different species (Urlus et al. 2014, Swan et al. 2014) and understanding how equipment varies is of considerable importance. Table 5. Mean and range (mm) of spoor measurements (Fig. 3) for three leopards hind left spoor, recorded under two substrate conditions in the field. Hind left: substrate < 2 mm Hind left: substrate > 2 mm 1 Min. 75 61 36 44 73 55 37 45 Mean 78 62 38 46 81 63 44 48 Max. 80 62 40 50 88 67 50 52 n 4 4 3 3 8 9 8 7 Leopard 2 Min. 85 55 45 45 89 58 45 45 Mean 87 60 47 47 93 63 52 51 Max. 90 63 49 49 97 70 58 60 n 3 3 2 2 16 15 16 16 Leopard 3 Min. 82 60 41 43 80 61 40 42 Mean 87 62 45 46 88 69 47 50 Max. 95 67 49 51 95 76 55 60 n 13 14 14 14 5 7 7 8 Assessing predator abundance and distribution in savannah remains a challenge. We argue that the traditional approach of using spoor and the skills associated with tracking in the field still have a place in monitoring the larger mammalian predators of savannah habitat in two key ways. First, spoor allows us to evaluate how effective camera traps are at recording the study species; our study species were 37% more likely to be recorded by spoor than by our camera traps. This study also supports the view that camera trap studies should be calibrated against other methods (Gompper et al. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the continuing relevance and effectiveness of traditional wildlife monitoring methods, such as spoor tracking and camera traps. It states that 'the traditional approach of using spoor and the skills associated with tracking in the field still have a place in monitoring the larger mammalian predators' and mentions that their study species were '37% more likely to be recorded by spoor than by our camera traps'. This indicates that traditional methods still provide valuable data and are not being replaced by eDNA technology.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Union represents an example where the different UAS regulations of each EU country are now unified under a common and simplified regulation (EU 2018/1139 and implementing acts). A review of worldwide UAS regulations is presented by Stöcker et al. (2017). Concurrently, advances in computer vision algorithms (Anderson et al. 2019) and hardware (e. g. , GPU) facilitated the use of photogrammetry through Structure from Motion (SfM) (Ullman 1979) and Multi-reconstruction (MVS) methods (Scharstein and Szeliski 2002). Today, SfM-MVS methods represent a core data capture and analysis approach widely used by earth and environmental scientists (Anderson et al. 2019; James et al. 2019). These technological and scientific advances facilitate the collection of centimeter-resolution continuous top-view data of shallow reefs at relatively low cost with more flexibility in the survey plan or experimental design (e. g. , Casella et al. , 2017). Although these are clear advantages for coral reef mapping and research, few studies surveying coral reefs with UASs and SfM-MVS exist compared to studies on terrestrial ecosystems (David et al. 2021; Fallati et al. 2020; Levy et al. 2018; Casella et al. 2017). The refraction of light by water, which causes features on the seafloor to appear shallower than they are may have led to this discrepancy because it introduces more significant errors than on-land environments. In addition, turbidity, water surface roughness, and the maximum depth for light penetration represent additional error sources (Joyce et al. 2018; Woodget et al. 2015). Some possible solutions to reduce the effect of the refraction of light on UAS SfM-MVS derived data exist or are being developed (Agrafiotis et al. 2020; Shintani and Fonstad 2017; Dietrich 2017; Ye et al. 2016; Woodget et al. 2015; Maas 2015). This study aims to investigate the relative accuracy of coral height reconstruction of two-media (air/water) SfM-MVS datasets derived from low-altitude UAS flights on shallow coral reefs (< 3 m depth). Rather then focusing on the geographic accuracy, our goal is to answer the question: “how accurately (relative accuracy) can we reconstruct coral height? ”. [SEP] Justify:

Output: The text is irrelevant to the claim about coral reefs supporting diverse marine species. It discusses the use of UAS (Unmanned Aerial Systems) and photogrammetry technologies for mapping coral reefs, as well as challenges faced in that context, without mentioning anything about marine species diversity or the ecological role of coral reefs.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: The recurrent input from high rate neurons can lead to a damped oscillatory response in the rest of the population (Figure 3A). The mean-field analysis shows that the strength of the damped oscillatory response is proportional to the strength of postsynaptic synaptic plasticity fR(ξi) in the case of linear dynamics. If fR for neuron i is positive (negative) corresponding to potentiation (depression) in recurrent inputs, an oscillation in neuron i would be in phase (out of phase) with that of high rate neurons. Previously, we proposed Hebbian-type but overall depression-dominant synaptic plasticity in recurrent connections to minimally account for the decrease in time-averaged responses (Lim et al. , 2015). However, this would lead to out of phase oscillation in the mean and maximum response, inconsistent with the data (Figure 1A,B). Instead, overall potentiation in recurrent inputs with f¯R >0 is required to generate in-phase oscillation in the mean and maximum response in linear dynamics (Figure 3—figure supplement 1). Additional synaptic plasticity for reduction in average response We showed that recurrent synaptic plasticity could account for the emergence of damped oscillation and sharpening neural activities by increasing the maximal response after learning. Furthermore, synchronous oscillations in the mean and maximum response observed experimentally suggest overall potentiation in recurrent inputs. However, potentiation-dominant synaptic plasticity in recurrent connections would increase overall synaptic input and cannot reproduce a decrease in average activities with learning (Figure 3—figure supplement 1A,B). The same holds for recurrent synaptic plasticity with or without the assumption of the constant sum normalization which imposes a constraint on the pre-synaptic dependence (Figure 3—figure supplement 2). Instead, reduction in average response requires changes in external inputs or other recurrent inputs such as suppression in other excitatory inputs or enhanced inhibition. Enhanced recurrent inhibition can result from an increase in inhibitory activities after learning or potentiated inhibitory connections onto excitatory neurons. The former is inconsistent with the experimental observations showing a reduction in inhibitory firing rates across different stimuli (Figure 1—figure supplement 1). [SEP] Justify:

Output: The text corroborates the claim by discussing synaptic plasticity in detail, specifically how it relates to memory formation. It describes how the strength of postsynaptic synaptic plasticity is important for neural responses, indicating that overall potentiation in recurrent inputs is required to result in effective synaptic plasticity. Furthermore, it mentions 'recurrent synaptic plasticity could account for the emergence of damped oscillation and sharpening neural activities by increasing the maximal response after learning,' which aligns directly with the claim that memory formation involves the strengthening of connections.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: 2006, Balme et al. 2009) in order to gain more accurate data to determine actual density, relative abundance and movements of species. Camera traps are beneficial but their reliability in capturing each animal that triggers the sensors is uncertain and depends on the camera model, habitat and as we show, species. Second, while it is unlikely that simple direct spoor measurements can be used to reliably identify unknown leopards due to interobserver variation, such spoor measurements enable known individuals to be followed by experienced field trackers with some certainty, allowing researchers to link records at static camera trap locations with the wider movements of an individual. We therefore suggest that while camera traps are an exceptionally helpful addition to the ecologist's armoury, we should take care to ensure that the benefits of good field craft are not neglected. are very grateful to and who made significant contributions to data collection. A special thank you to the late Ronnie Watson, who championed Research efforts, and to Alan and Lynsey Watson, Will and Carol Fox, the share-holders and staff of and PAW for their support. 2008. A footprint technique to identify white rhino Ceratotherium simum at individual and species levels. —Res. 4: 205–218. 10. 3354/esr00067 2014. Camera traps, sand plots and known events: what do camera traps miss? —In: Meek, P. and Fleming, P. (eds), Camera trapping: wildlife management and research. —. , 202. 2009. Evaluating methods for counting cryptic carnivores. —J. Wildl. Manage. 73: 433–441. 10. 2193/2007-368 Web of Science®1996. Power of track surveys to detect changes in cougar populations. —Wildl. Soc. Bull. 24: 540–546. Web of Science®1984. Aspects of the ecology and the behaviour of the leopard Panthera pardus in the Kalahari desert. —Koedoe 27: 259–279. 10. 4102/koedoe. v27i2. 585 2010. An estimate of leopard population density in a private reserve in KwaZulu-Natal, South Africa, using camera-traps and capture—recapture models. —S. Afr. J. Wildl. Res. 40: 114–120. 10. 3957/056. 040. 0202 Web of Science®2010. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the utility of camera traps and other traditional wildlife monitoring methods. It discusses their benefits, including accuracy in tracking species movements, while also addressing limitations, such as reliability issues and the necessity of good field craft. By stating that camera traps are 'an exceptionally helpful addition to the ecologist's armoury' and emphasizing the value of techniques like spoor measurements, the text underlines that traditional methods are still significant, hence challenging the idea that eDNA technology will entirely replace them.


Input: Task: pass the claim using the given text. Claim: Coral reefs support a diverse range of marine species. [SEP] Text: The Phantom-4 Pro has an integrated RGB sensor 1’’ CMOS (effective pixel: 20 M), a focal length of 9 mm, and a resolution of 5472 × 3648 pixels (as per technical specifications: dji. com/phantom-4pro). Nine different flights were programmed to survey the sites at an altitude of 10 m (see Supplementary Table). app (dji. com/de/ground-station-pro) was used to program the UAS flights so that each flight resulted in a 90% forward and lateral overlap. Two types of markers were placed on the seafloor of each site before the flights (Fig. 1c, d): ground control points (GCPs) and scale bars (SBs). A total of 45 30 × 30 cm bright-colored towels and 29 50 cm-long steel bars covered by bright-colored tape were used as GCPs and SBs, respectively. The geographic position of GCPs was collected using a handheld GNSS receiver (Garmin eTrex® 10) with a horizontal accuracy in the order of meters, and their vertical position (local depth) was measured with a handheld Hondex PS-7 dive sonar with reference to the local water level at the time of measurement. The position of GCPs and the length of SBs were used to scale the point cloud generated during the SfM-MVS process. The position of GCPs was also used to georeference the point cloud, optimize the image alignment, and minimize the sum of the re‐projection error and the georeferencing error of the estimated internal camera parameters and point cloud in the SfM-MVS method. The acquired photographs, GCPs, and scale bars were used as inputs in the SfM-MVS method through 1. 6. 2 (agisoft. com) to reconstruct a 3D model of the scene captured by aerial photographs. Specifically, DSMs and orthomosaics of each reef were reconstructed (Fig. 1d, e). For a comprehensive description of the SfM-MVS method, the reader is referred to Westoby et al. (2012) and Carrivick et al. (2016). To validate the DSMs, IMs were collected at each site. Over the entire study area, the height of 51 corals was measured with a meter rod in situ to the nearest centimeter. [SEP] Justify:

Output: The text is irrelevant to the claim as it provides technical specifications and methodologies for a study involving aerial photography and mapping of reef areas, but does not mention marine species or the biodiversity supported by coral reefs.


Input: Task: corroborate the claim using the given text. Claim: Memory formation involves the strengthening of neural connections, a process known as synaptic plasticity. [SEP] Text: Also, potentiated inhibition with learning is less likely to account for a decrease of average excitatory activities - a temporal profile of inhibitory activities after learning shows a decrease of activity almost to the baseline in the late phase of the stimulus presentation (200–250 ms after the stimulus onset). This suggests that the effect of potentiated inhibition in the late phase is weaker than in the early phase while reduction of excitatory activities was observed in the late phase (Figure 1A,B). Another possibility is a depression in recurrent excitation through different types of synapses such as potentiation in fast AMPA-like currents and depression in slow NMDA-like currents. Depression in slow excitatory currents can lead to a decrease in excitatory activities in the late phase. However, different regulation of AMPA and NMDA currents is inconsistent with the experimental observations showing maintenance of a constant NMDA-to-AMPA ratio under the changes of AMPA receptors induced chemically or by an STDP protocol (Watt et al. , 2000; Watt et al. , 2004). Instead of additional changes of the recurrent synaptic inputs, we considered changes in external inputs with feedforward synaptic plasticity ΔWijF=fF(ξi)gF(ξj). Together with overall potentiation in the recurrent connections, dominant depression in the feedforward connections with f¯F< 0 can reproduce the reduction of average responses over the stimuli with learning. 4, an example network with Hebbian learning rule in recurrent connections, uniform depression in the feedforward connection, and spike adaptation mechanisms was shown to reproduce the effects of visual learning qualitatively. With learning, the average response decreases in particular in the late phase (Figure 4A), but maximal firing rates increased and oscillation becomes prominent especially in high rates (Figure 4B,C). Also, in the successive presentation of two stimuli, the average response shows stronger oscillation after learning (Figure 4D), while the rank of individual neuronal activities changes when a new stimulus arrives (Figure 4E,F). Figure 4 Download asset Open asset Example network reproducing the effects of visual learning in one stimulus presentation (A–C) and successive presentation of two stimuli (D–F). The network implements potentiation in the recurrent connections through Hebbian synaptic plasticity, depression in the feedforward connections through uniform scaling down of the external inputs, and spike-adaptation mechanisms. [SEP] Justify:

Output: The text corroborates the claim as it discusses mechanisms related to synaptic plasticity, such as 'potentiation in the recurrent connections through Hebbian synaptic plasticity' and the effects of learning on neural response, suggesting changes in synaptic connections occur as a result of learning. This aligns with the claim that memory formation is linked to the strengthening of neural connections.


Input: Task: contrast the claim using the given text. Claim: eDNA technology will soon replace all traditional wildlife monitoring methods, such as camera traps or field surveys. [SEP] Text: Censusing and monitoring black rhino (Diceros bicornis) using an objective spoor (footprint) identication technique. —J. Zook 254: 1–16. 10. 1017/S0952836901000516 Web of Science®1995. Estimating tiger Panthera tigris populations from camera-trap data using capture—recapture models. —Biol. Conserv. 71: 333–338. 10. 1016/0006-3207(94)00057-W Web of Science®1998. Estimation of tiger densitites in India using photographic captures and recaptures. —Ecology 79: 2852–2862. 10. 1890/0012-9658(1998)079[2852:]2. 0. CO;2 Web of Science®2003. Science deficiency in conservation practice:the monitoring of tiger populations in India. —Anim. Conserv. 6: 141–146. 10. 1017/S1367943003003184 Web of Science®2008. Design, evaluate, refine: camera trap studies for elusive species. —Anim. Conserv. 11: 182–184. 10. 1111/j. 1469-1795. 2008. 00179. x Web of Science®1997. field guide to African mammals. —Academic Press. 10. 1111/j. 1744-7429. 1998. tb00369. x 2001. Validation of a rigorous track classification technique: identifying individual mountain lions. —Biol. Conserv. 99: 313–321. 10. 1016/S0006-3207(00)00197-X Web of Science®2005. A field guide to the animal tracks of southern Africa; and Johannesburg. —Publ. 2008. Comparing methods for sampling large- and medium-sized mammals: camera traps and track plots. —Eur. J. Wildl. Res. 54: 739–744. 10. 1007/s10344-008-0205-8 Web of Science®2013. Calibrating a camera trap—based biased mark—recapture sampling design to survey the leopard population in the N'wanetsi concession, Park, South Africa. —Afr. J. Ecol. 51: 422–430. 10. 1111/aje. 12047 Web of Science®2006. [SEP] Justify:

Output: The text contrasts the claim by discussing various traditional wildlife monitoring methods, such as camera traps and spoor identification techniques. It emphasizes the continued importance and utilization of these methods in monitoring species like the black rhino and tiger, suggesting that traditional methods are still relevant and not being replaced by eDNA technology.