Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Averted loss is estimated in relation to a counterfactual, those changes in biodiversity that are predicted to have occurred if the site were not managed to deliver an offset [1], and is the absolute expected future losses that would occur in the absence of an offset (AL = current value–future value without offset). The choice and quantification of the counterfactual is fundamental to estimating the potential benefits arising from conservation actions at offset sites. In most cases the counterfactual is assumed to be an on-going background rate of decline in biodiversity [8]. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 1. The total benefit of implementing conservation management is the sum of averted losses and additional gains above the current value. The top solid line represents trends in conservation value with offset and the lower solid line represents the trends under the counterfactual. While our eli10. 1371/journal. pone. 0216703. g001Much attention has been paid to the principles underlying NNL [2, 9, 10], but relatively little to the actual quantification of changes under offset schemes [2]. This is surprising given the predicted magnitude of change is often used directly to determine the scope of the offset in the field (e. g. its size and the degree of investment in its management). Because the outcomes of conservation management and restoration are highly variable, hard to predict and difficult to generalise [11, 12], the risks of not achieving adequate compensatory gains, within an appropriate time-frame, can be considerable. Estimating plausible future values and the uncertainty around them is a requirement in some offset schemes [13–15], although in other circumstances offset schemes use fixed multipliers or “rules of thumb” rather than explicit estimates [16]. Importantly, because estimates are rarely derived in a structured and consistent manner they are open to manipulation and inconsistency [17]. While acknowledgment of this issue is widespread, few studies have attempted to quantify the ecological outcomes of conservation management at offset sites in part because of a lack of generalisable data, models or monitoring of sufficient duration [10]. Because we have little data to inform predictions of future changes in biodiversity value, expert elicitation becomes a useful tool for developing hypotheses for where and when offsets have greatest potential for generating benefits. [SEP] Justify:

Output: The text contrasts the claim by explaining that the outcomes of conservation management and restoration are 'highly variable, hard to predict and difficult to generalise.' It highlights the considerable risks of not achieving adequate compensatory gains, suggesting that offsets do not guarantee full restoration of lost habitats. The mention of the unpredictability and variability of conservation outcomes indicates that biodiversity offsetting cannot fully restore habitats as claimed.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Meanwhile, because of the decrease of boundary layer friction and turbulent mixing that results in the absence of solar heating, a perturbation low-level southwesterly nocturnal jet has fully developed (Figs. 5c and 6c). This enhances the low-level warm-, moist-air advection from the southwest as well as low-level lifting, both of which are likely responsible for the nocturnal precipitation maximum over the plains (Fig. 3e). By now, strong downslope flows (Fig. 6c) have led to a precipitation minimum over the slopes and foothills (Fig. 3e). A low-level southerly nocturnal jet, parallel to the mountain ranges, is also widely observed over the of the (e. g. , Bonner 1968) and has at least in part been a cause of nocturnal precipitation maxima (e. g. , Higgins et al. 1997). At 0000 UTC (0800 LST), a few hours after sunrise, the upward branch of the remnant MPS circulation remains over the central plains but is substantially weakened while the perturbation low-level southwesterly jet has been completely replaced by the north-northwesterly downslope winds (Figs. 5d and 6d). There is only a very weak signature of the diurnal precipitation peak associated with this upward branch. The minimum precipitation phase is observed over most of the focus domain, except at the eastern corner where the ocean has a strong influence (Fig. 3f). The above diagnosis performed with the GFS analysis demonstrates a strong connection between the evolution of peak diurnal precipitation and the upward branch of an MPS circulation, progressing southeastward from the top of the mountain ranges in the early afternoon to the central Plains in the early morning. The 13 m s−1 propagation speed and direction correspond to a midtropospheric steering level at around 500 hPa (not shown). [SEP] Justify:

Output: The text is primarily focused on discussing specific meteorological phenomena such as low-level jets, precipitation patterns, and diurnal effects related to weather without providing any information that directly supports or contradicts the idea that microclimates can create weather variations over short distances. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Sleep is crucial for memory consolidation, especially when learning new skills or information. [SEP] Text: Due to the importance of learning new words in a second language (generally English) in Iran, the challenge of remembering new words, and the lack of a comprehensive study in Iran, we decided to investigate the role of sleep in stabilizing memory and learning new words of a second language (English) in Persian speakers using the methodology described by Kurdziel and Spencer [6]. In this study 45 words were drawn from the Project database. Participants were divided into two groups. Subjects completed two sessions of study. Four experimental phases were spread across two sessions (passive encoding, active encoding, and immediate recall occurring during session 1 and delayed recall during session 2). The wake group started Session 1 in the morning (between 8–10 AM). Session 2 was performed 12 hours later in the evening. The sleep group started Session 1 in the evening (between 8–10 PM). Session 2 occurred 12 hours later in the following morning. All participants’ sleep parameters were evaluated by and Scale (SSS). This research showed that sleep not only promoted newly learned word-definition pair’s retention, but also facilitated the retention of items that were inaccurate but close to correct. By designing this study, we tried to provide useful suggestions for Persian-speaking people to learn English with a high efficiency. study was performed using the methodology described in the study of Kurdziel and Spencer [6]. Forty Persian-speaking male adults aged between 18 and 35 years who were learning English as their second language with an intermediate English language level with at least six hours of sleep at night participated in this study. They were learning English in an English language school. After meeting them in person and explaining the study process, they decided to participate in the study. Subjects were divided into two groups by the head of the educational institution. Subjects had no history of neurological diseases or sleep disorders. They had an average of six hours of sleep a night. They did not take any medications that might interfere with their sleep process. For this study, individuals were randomly divided into two groups. The first group learned new words between 8 and 10 in the morning. Cognitive assignments and second stage tests were performed between 8 and 10 in the evening. group learned new words between 8 and 10 PM. Cognitive tasks of the first stage were performed at night. Cognitive measurements of the second stage were performed between 8 and 10 in the next morning after sleeping at night. The researchers had no role in this random selection. [SEP] Justify:

Output: The text corroborates the claim by detailing a study that investigates the role of sleep in memory retention, specifically in the context of learning new words in a second language. It highlights that sleep not only facilitated the retention of newly learned word-definition pairs but also helped retain items that were incorrect but close to correct, underlining the importance of sleep in the memory consolidation process linked to learning new information.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Expert judgment is heavily relied upon in many areas of environmental decision making, especially when knowledge is incomplete, there is substantial uncertainty and urgent predictions are required, precluding reliance on long-term experiments or monitoring [18]. Most elements of offset design are dependent to some extent on belief, opinion and value judgement, and informal expert opinion guides many aspects of NNL policies (i. e. choice of metrics, equivalency and location rules, predictions of benefits from conservation actions, estimates of uncertainty and time lags). However, rarely are offset provisions informed by a structured and explicit elicitation of opinion and belief, which considers variation and uncertainty. Here we report the results of an expert elicitation with 29 vegetation field ecologists, practitioners, research scientists and ecological consultants on their predictions about the likely changes in biodiversity indicators in response to management of threats and pressures at hypothetical biodiversity offset sites. While expert opinion will not substitute for empirical data, it can highlight important sources of variation and provide useful guidance for identifying when and where offsetting has most potential for success but also where we should be most wary, and where empirical data are most urgently needed. We expected large variation among experts in their absolute predictions, but we hypothesised that experts would share similar opinions about those attributes and scenarios where benefits from offsetting would most likely accrue. and selection of experts Our aim was to select a pool of experts who had extensive practical and/or theoretical knowledge of vegetation dynamics in south-eastern Australia. From an initial pool of 94 native vegetation professionals, 39 experts were invited to self-rate their experience in vegetation survey and monitoring through an online questionnaire and asked to participate in one of three two-day workshops (see S1 File). In total, 37 experts completed the online survey, and 29 attended the workshops. The focal ecosystem Our online survey revealed that most experts had experience in the ecology, management and monitoring of grassy woodlands (S1 File) and for this reason we chose to primarily focus the elicitation on this ecosystem. During the workshops opinions were also elicited for other vegetation types, although results did not reveal differences among them (S2 File). The focal ecosystem for the eli1. Individual vegetation condition attributes that formed the basis for the structured elicitation. The elicitation We used two separate approaches to elicit expert opinions about current values and future values in vegetation condition. Firstly, we captured expert point predictions about the current and future aggregate condition of a vegetation patch. [SEP] Justify:

Output: The text contrasts the claim by highlighting the limitations and uncertainties inherent in the process of biodiversity offsetting. It states that expert judgment is often based on belief, opinion, and value judgments, suggesting that without structured and empirical data, it is difficult to guarantee that biodiversity offsetting can fully restore lost habitats. The mention of uncertainty and the need for empirical data points to the challenges of achieving true restoration, thus undermining the claim.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Geophys. Res. , 104, 6377– 6402. )| falseSearch 744. Dai, A. , X. Lin and K-L. Hsu 2007: The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes. Climate Dyn. , 29, 727– 744. )| falseSearch 507. Higgins, R. W. , Y. Yao E. S. Yarosh J. E. Janowiak and K. C. Mo 1997: Influence of the low-level jet on summertime precipitation and moisture transport over the central United States. J. Climate, 10, 481– 507. )| falseSearch 3105. doi:10. 1029/2005JD006156. Janowiak, J. E. , V. E. Kousky and R. J. Joyce 2005: Diurnal cycle of precipitation determined from the high spatial and temporal resolution global precipitation analyses. J. Geophys. Res. , 110, D23105. doi:10. 1029/2005JD006156. )| falseSearch 503. Joyce, R. J. , J. E. Janowiak P. A. Arkin and P. Xie 2004: : A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor. , 5, 487– 503. )| falseSearch 933. Koch, S. E. , F. Zhang M. L. Kaplan Y-L. Lin R. Weglarz and C. M. Trexler 2001: Numerical simulations of a gravity wave event over CCOPE. Part III: The role of a mountain–plains solenoid in the generation of the second wave episode. Mon. Wea. Rev. , 129, 909– 933. )| falseSearch 2415. Lin, Y. , and F. Zhang 2008: Tracking mesoscale gravity waves in baroclinic jet-front systems. J. Atmos. Sci. , 65, 2402– 2415. )| falseSearch 168. Qian, W. , H. Kang and D. Lee 2002: Distribution of seasonal rainfall in Monsoon region. Theor. Appl. Climatol. , 73, 151– 168. [SEP] Justify:

Output: The text does not address the claim regarding microclimates and their effect on weather variations over short distances. It includes references to various studies and their findings but does not provide any relevant information about microclimates.


Input: Task: corroborate the claim using the given text. Claim: Sleep is crucial for memory consolidation, especially when learning new skills or information. [SEP] Text: Evaluation of the relationship between the mean score of subjective quality of sleep scale with the mean error rate of early LD in all participants showed a significant decrease in the number of words memorized in people who had a poor quality of sleep (p = 0. 03). After examining the correlation between quality indicators of sleep in the Pittsburgh questionnaire on immediate and delay recalls of words in two groups of tests separately, it was found that subjective quality of sleep, sleep duration more than 5 hours and 45 minutes, and no delay in falling asleep had some positive but insignificant effects on the amount of immediate recall in the two groups. On the other hand, the duration of adequate sleep had a significant effect on the amount of vocabulary delay recall in the second group (p = 0. 007, r = -0. 581) (Table 3). Results of analyzing the effect of subjective sleep quality on the error rate of delayed LD showed that although the average number of delayed LD errors was lower in people with adequate sleep quality, this difference was not statistically significant. Analyzing the effect of sleep duration on delayed LD error showed a significant LD error difference between people with appropriate sleep length and those with an inappropriate sleep length (f = 9. 1, df = 1, p = 0. 005). Repeated measure analysis of variance (Greenhause-gaseer: df = 14, f = 3. 92, p-value = 0. 002) showed that people who slept more than 5 hours and 45 minutes could remember more words than those who slept less in the second stage of the test (Fig. 1). general result of the present study showed an effect of night sleep on the level of recall of new English words in Persian speaking people. Results of the Stanford questionnaire indicated that study participants did not have drowsiness while doing their homework. Therefore, the role of drowsiness was not analyzed in this study. However, some indicators listed in the Pittsburgh sleep quality questionnaire showed significant effects on the elevation of LD error rate. People who had poor quality of sleep and those who had sleep duration of less than 5 hours and 45 minutes committed more LD errors. As a final result, a good sleep could significantly reduce the LD error rate. Several factors have been identified to be able to strengthen memory, resulting in people’s ability to remember words, which is an important part of the language learning process. One of these factors is sleep. Recent studies have focused a lot on sleep as an important factor. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the significant effects of sleep duration and quality on memory recall, particularly in the context of language learning. It highlights that individuals who experience adequate sleep (more than 5 hours and 45 minutes) could remember more words during tests, thus supporting the assertion that sleep is crucial for memory consolidation.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: This approach has been shown to usefully capture complex belief models of existing habitat value [29, 30], though the method has not yet been extended to future predictions. This elicitation exercise was completed by all 29 experts. Secondly, we elicited from twenty-five experts, the probability densities of future values of individual vegetation attributes. To do this we used the trial roulette or histogram method [31], which allowed experts to graphically generate a probability histogram to represent their belief about future values. We used this approach to extract the maximum information about central tendency, the range of predicted values, and the degree of spread. Evaluating current and future aggregate vegetation condition. Our approach followed that of Sinclair and colleagues [29, 30] in that experts were asked to rank and score synthetic WSGW vegetation patches (‘sites’), based on their current and future contributions to persistence of biodiversity, assuming this could be captured by a single aggregate value representing vegetation condition. et al. [29, 30] our primary aim was not to generate a metric for predicting vegetation condition but rather to explore expert opinion about change in the aggregate value over time. We sought to examine whether experts generated consistent current and future estimates for comparable sites and identify those covariates correlated with their predicted changes. A pool of 64 synthetic WSGW sites, each assumed to be 2ha, was informed by available full-floristic plot data for this bioregional vegetation type in the NSW BioNet database (www. bionet. nsw. gov. au). Each site description included an estimate of native vegetation cover within a 1. 5km radius of the patch, the percent cover of invasive alien plant species [32], total alien plant ground cover, the dominant plant species in each of five growth forms (trees, shrub, forbs, grasses, ferns) and the current values of each of the 13 vegetation attributes (Table 1, see example in S3 File). Experts were asked to assume that these data had been estimated from a randomly placed 20m x 20m plot (richness and foliage cover of each growth form) or 20m x 50 m plot (habitat structure attributes). The 64 sites covered a wide range of conditions including those representing species-rich remnants through to disturbed patches dominated by exotic vegetation. Experts were each provided with a randomly-drawn subset of 10 sites and five that were common to all experts. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of capturing habitat value and predicting future vegetation conditions. It highlights that the methodology used involves expert opinions on current and future conditions of vegetation, implying that a straightforward restoration to original conditions may not be feasible. Furthermore, it emphasizes the goal of understanding changes over time rather than ensuring full restoration of habitats, suggesting limitations in biodiversity offsetting as a means to restore habitats fully.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: )| falseSearch 2461. Trier, S. B. , C. A. Davis D. A. Ahijevych M. L. Weisman and G. H. Bryan 2006: Mechanisms supporting long-lived episodes of propagating nocturnal convection within a 7-day WRF model simulation. J. Atmos. Sci. , 63, 2437– 2461. )| falseSearch 328. Tripoli, G. J. , and W. R. Cotton 1989: Numerical study of an observed orogenic mesoscale convective system. Part II: Analysis of governing dynamics. Mon. Wea. Rev. , 117, 305– 328. )| falseSearch 419. Wallace, J. M. , 1975: Diurnal variations in precipitation and thunderstorm frequency over the conterminous United States. Mon. Wea. Rev. , 103, 406– 419. )| falseSearch 1629. Wang, C. C. , G. T. J. Chen and R. E. Carbone 2004: A climatology of warm-season cloud patterns over east Asia based on GMS infrared brightness temperature observations. Mon. Wea. Rev. , 132, 1606– 1629. )| falseSearch 1508. Wolyn, P. G. , and T. B. McKee 1994: The mountain–plains circulation east of a 2-km-high north–south barrier. Mon. Wea. Rev. , 122, 1490– 1508. )| falseSearch 1170. Yin, S. , D. Chen and Y. Xie 2009: Diurnal variations of precipitation during the warm season over China. Int. J. Climatol. , 29, 1154– 1170. )| falseSearch 1704. doi:10. 1029/2006GL028129. Yu, R. , T. Zhou and A. Xiong 2007: Diurnal variation of summer precipitation over contiguous China. Geophys. Res. Lett. , 34, L01704. doi:10. 1029/2006GL028129. )| falseSearch 2796. Zhang, F. , and S. E. Koch 2000: Numerical simulation of a gravity wave event observed during CCOPE. Part II: Wave generation by an orographic density current. Mon. Wea. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses various numerical studies and analyses related to weather and precipitation without addressing the concept of microclimates or their impact on weather variations over short distances.


Input: Task: corroborate the claim using the given text. Claim: Sleep is crucial for memory consolidation, especially when learning new skills or information. [SEP] Text: The effect of sleep on language learning has been examined with several contents such as lexical learning [4], speech recognition [13], the role of sleep in the consolidation of newly learned data’s [14], and so on. Sleep deprivation is also one of the most important problems that can cause functional disorders in people and children’s concentration, as a fundamental issue in schools [15-17]. Effects of sleep deprivation are usually ignored as they are usually partial and end with sleep. The duration of sleep deprivation and its recurrence over a short period of time can increase the burden of problems. Buxton et al. [18] have shown that sleeping between sessions can enhance inter-session learning. They found that this method could be a simple and reliable method to achieve a long reading and reminder with less time for study. In their study, results of reading in two turns (after one week and after six months) were evaluated and the optimal effect of sleep on reading was proven [19], which was in line with results of the present study showing that the number of recalled words in the second stage of the second group showed a very significant difference in new words recall after night session in comparison with morning session. Effects of sleep on learning and memory have been proven by demonstrating structural changes using non-invasive neuroimaging methods in the brain which emphasizes the effect of sleep on memory processes [20], consistent with results of the present study showing that the sleep process plays a role in vocabulary recall. Additional studies are needed to investigate relationships of memory stabilization stages with short-wave sleep and REM sleep periods. In some studies, the effect of early and late sleep on visual memory has been investigated. SWS waves have been effective in early sleep and REM memory in late sleep. Visual memory is more affected by early sleep and in fact, the formation of visual memory is more simultaneous with SWS waves [8]. Due to the lack of polysomnography documents in the current study, it is not possible to provide comparable results with the above-mentioned study. A previous study by Kurdziel and Spencer [6] on the relationship between sleep and memory has shown that volunteers who take the first three phases of the test at night and the fourth phase 12 hours later (and at least 6 hours after a nap) can recall more words than volunteers who have completed the first three steps at 8 AM and completed the fourth step 12 hours later without a nap. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that sleep plays a significant role in memory processes and consolidation of newly learned data. It discusses studies showing how sleep deprivation affects concentration and learning capability, and notes that sleep between learning sessions can enhance inter-session learning. Additionally, the reference to neuroimaging methods demonstrating structural changes in the brain due to sleep further supports the claim that sleep is crucial for memory consolidation.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: The five common sites were selected a priori by the authors and were assumed to represent a range from expected high through to expected low quality. Experts were not aware that their set included five common sites. These five sites were included to ensure that all experts were considering sites across the full spectrum of variation in the ecosystem, as a means of calibrating their judgements [29, 30]. The experts were initially asked to rank the sites in order of current value to persistence of biodiversity, not considering costs of management or potential for future changes (improvement or degradation) in the vegetation. Experts were then asked to provide each site with a vegetation condition score out of 100 where 100 represented highest value and 0 held no value to biodiversity. Experts were told that multiple sites could be given the same score, that scores could be distributed in any way they desired and that values of 0 and 100 did not have to be used. Following estimation of current value, experts were asked to provide estimates of the vegetation condition score (out of 100) 20 years into the future. Experts were asked to estimate a future vegetation condition score for each site assuming they were managed under an offset agreement. An offset agreement was assumed to require that the site was permanently secured for conservation management and that all major site-based threats and pressures were managed, if practicable, to maximise biodiversity outcomes. This was assumed to include monitoring and management of weeds and exotic pests, management or exclusion of livestock grazing as appropriate to best achieve conservation outcomes, implementation of appropriate fire regimes and retention of all fallen timber and dead trees. Experts were asked to assume no seeding or supplementary planting were undertaken. This combination of management activities is typical of current mandatory offset obligations under the 2016 [33]. Experts at both the 2nd and 3rd workshops (16 experts) were also asked to provide a future condition score which reflected their best estimate if the sites in question were managed under a business as usual (BAU) scenario. BAU assumed a continuation of existing management practices. Trial roulette elicitation of individual vegetation attribute probability distributions. The trial roulette method was used to obtain an expert’s distribution of predicted future outcomes for individual vegetation attributes under a BAU scenario and with a biodiversity offset. [SEP] Justify:

Output: The text contrasts the claim by discussing various methods and expert assessments regarding habitat management under biodiversity offset agreements. It outlines that while certain management practices can aid in improving biodiversity outcomes, the assumption of fully restoring the original habitat to its prior condition is not supported by expert estimations, especially since no seeding or supplementary planting were undertaken. This implies that biodiversity offsetting may not achieve the full restoration intended by the claim.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Rev. , 128, 2777– 2796. )| falseSearch 4010. Zhou, T. , R. Yu H. Chen A. Dai and Y. Pan 2008: Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations. J. Climate, 21, 3997– 4010. )| falseSearch 1. (a) Study domain map and terrain distribution (shaded every 200 m) over northern China with the focus area highlighted in box ABCD, which includes the cities of Beijing (denoted as BJ) and Tianjin (TJ) and the provinces of Shanxi (SX), Inner-Mongolia (NM), Hebei (HB), Shandong (SD), Henan (HN), Anhui (AH), and Jiangsu (JS). (b) Distribution of mean warm-season hourly rainfall rate (mm h−1) averaged over all hours from May to August during 2003–09 estimated with the dataset. (c) Averaged terrain elevation and hourly precipitation rate along the northwest–southeast direction averaged over the boxed area in (a). The starting point of the cross section is an average over line AB, and the ending point is an average over line CD; the same averaging method will be used for Figs. 4 and 6 along the same cross section. Citation: Review 138, 4; 10. 1175/2010MWR3356. 1Download FigureDownload figure as PowerPoint slide Size Fig. 2. (a) Percentage of the diurnal contributions of the total precipitation (calculated as the sum of the absolute differences between the mean precipitation rate at each hour and the mean hourly precipitation rate divided by the mean hourly precipitation rate). The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. (b) Isochrones (phase fronts) of the local diurnal precipitation peaks at different times of Fig. 3 with terrain elevations shaded every 200 m. The time label of the isochrones in (b) is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. Citation: Review 138, 4; 10. 1175/2010MWR3356. 1Download FigureDownload figure as PowerPoint slide Size Fig. 3. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on a study regarding summer precipitation frequency, intensity, and diurnal cycles over China without mentioning microclimates or how they affect weather variations over short distances.


Input: Task: corroborate the claim using the given text. Claim: Sleep is crucial for memory consolidation, especially when learning new skills or information. [SEP] Text: Results of the present study are in line with the study on the role of sleep in learning words with lexical similarity by Dumay and Gaskell [4]. They emphasized that words learned at 8 pm after 12 hours (after a night’s sleep) could induce vocabulary competition correctly and that subjects could memorize words in the medium term and long term. However, when here is no night-time sleep after learning, this process was transferred to 24 hours later (after establishing a night-time sleep period) [4]. In line with these findings, some studies have carefully examined effects of insomnia on learning a new language. For example, Pilcher et al. [21] have conducted a study on non-English speakers using several cognitive and linguistic tests and found that sleep deprivation has a negative impact on linguistic abilities. Results obtained in the present study are somewhat in line with results reported previous, especially those using Pittsburgh sleep quality indicators. Of course, the above studies are different in terms of inclusion criteria and implementation method, including the degree of difficulty of words and observing the exact hours of sleep, sample size, age of subjects, length of delayed reading time, and so on. However, most studies have observed a significant effect of adequate sleep on memory stabilization. Differences in results are partly due to differences in implementation methods and human errors or the boredom of candidates in answering questions of the questionnaires which are unavoidable in human studies. Conclusion Overall, based on results of this study, it seems that having a good night’s sleep is effective in improving and stabilizing memory. Mental quality of sleep, delay in falling asleep, duration of sleep, and quality of sleep can also have a significant impact on this process. Analytical analysis of PQSI questionnaire results showed that people with enough sleep (more than 5 hours and 45 minutes of sleep) a night have a much better ability to read delayed than people with insufficient sleep. This shows the effect of sleep on memory stabilization. Our findings support the hypothesis that more second language words can be recorded in immediate and delayed memory along with adequate sleep time. We recommend the use of results of the present study to non-native language teaching institutions in order to improve the practicality of educational results. study is a human-based research study. We had some limitations about controlling the whole issue especially when tests were done in their home. Thus, we tried to have phone consultation for running the test using the right methodology. [SEP] Justify:

Output: The text corroborates the claim by discussing how adequate sleep has significant effects on memory stabilization and learning, particularly in the context of language acquisition. It emphasizes that results across multiple studies support the idea that having a good night’s sleep enhances the ability to memorize both immediate and delayed information. Specifically, it states that people who sleep sufficiently can perform better in reading delayed tasks than those who do not, reinforcing the assertion that sleep is essential for memory consolidation.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: The scenarios for which future values were elicited are summarised in Table 2. For each scenario experts were provided with brief descriptions of a hypothetical 2ha site, including starting values of each of the vegetation attributes, monthly rainfall (median, 10th and 90th percentiles), estimates of the cover of invasive alien plant species, and dominant species in each growth form (see example in S3 File). Experts were asked to assume that the site occurred within a landscape with >70% native vegetation cover (in a 1. 5km buffer around each patch) but was not within the current nature conservation estate. Each site was assumed to represent a relatively homogenous area of the same vegetation type and similar condition state. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. The three (WSGW) scenarios for which future values of 13 vegetation attributes were elicited (also see S3 File). one WSGW scenario, experts were asked to predict changes in the vegetation attributes after 20 years of BAU management, and assuming the patch was instead secured and managed as a biodiversity offset. This enabled calculation of averted loss, management gain and total benefit as per Fig 1. For all remaining scenarios, experts were only asked to make predictions assuming that patches were secured as biodiversity offsets, and so only management gain was calculated. This approach was taken to reduce expert fatigue during the workshops. We compared estimates of management gain among each of the three WSGW scenarios to see whether starting conditions influenced expert predictions. Data analysis Aggregate vegetation condition. Management gain was estimated for each of the expert’s 15 vegetation patches. For those where BAU outcomes were estimated then averted loss and total benefit were also calculated. The distribution of expert evaluations for each of the 64 vegetation patches were inspected using box and whisker plots, sorting individual sites by their median current vegetation condition. We used boosted regression trees (BRT) with the gbm. step function in the packages gbm [36] and dismo [37] within R [38] to undertake exploratory analysis of current vegetation condition, management gain, averted loss and total benefit. Covariates included all 13 vegetation attributes, landscape vegetation cover, invasive alien plant cover, total alien plant cover and expert identity. For models of management gain, averted loss and total benefit we included each expert's estimate of current vegetation condition as an additional covariate. [SEP] Justify:

Output: The text contrasts the claim by discussing various scenarios where biodiversity offsetting is implemented, yet it does not assert that these measures can fully restore the habitat to its original condition. Instead, the experts provided estimates of management gain, averted loss, and total benefits which suggest that while biodiversity offsets might improve conditions, they do not guarantee the complete restoration of original habitat states. The focus on management gain and analysis of current vegetation conditions implies limitations to what biodiversity offsetting can achieve.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Map distribution of normalized diurnal precipitation deviations at (a) 0600, (b) 0900, (c) 1200, (d) 1500, (e) 1800, and (f) 0000 UTC estimated as the mean precipitation rate at this hour minus the mean precipitation rate throughout the date, normalized by the standard deviation of hourly precipitation at each location. Scales smaller than 200 km are truncated by a 2D spectral decomposition. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima). Citation: Review 138, 4; 10. 1175/2010MWR3356. 1Download FigureDownload figure as PowerPoint slide Size Fig. 4. Distance–time Hovmöller diagram of the normalized hourly precipitation deviation without filtering averaged along the cross section from northwest to southeast (as in Fig. 1c). The dashed arrows show the southeastward phase propagation of the diurnal precipitation peaks. The time axis is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. Citation: Review 138, 4; 10. 1175/2010MWR3356. 1Download FigureDownload figure as PowerPoint slide Size Fig. 5. Spatial distribution of the 900-hPa vertical motions (colored) and perturbation wind vectors diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima) as in Fig. 3. Citation: Review 138, 4; 10. 1175/2010MWR3356. 1Download FigureDownload figure as PowerPoint slide Size Fig. 6. Vertical profiles of the averaged vertical motion (colored; cm s−1), the perturbation vertical circulation vectors (horizontal wind component along the cross section and 100 times the vertical velocity), and the horizontal wind component perpendicular to the cross section (contoured in blue every 0. [SEP] Justify:

Output: The text does not address the claim regarding microclimates and their impact on weather variations. Instead, it focuses on technical details about precipitation deviations and the spatial distribution of vertical motions, which do not provide evidence for or against microclimates affecting weather over short distances.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: periods of sleep loss have long-lasting consequences such as impaired memory consolidation. Structural changes in synaptic connectivity have been proposed as a substrate of memory storage. Here, we examine the impact of brief periods of sleep deprivation on dendritic structure. In mice, we find that five hours of sleep deprivation decreases dendritic spine numbers selectively in hippocampal area CA1 and increased activity of the filamentous actin severing protein cofilin. Recovery sleep normalizes these structural alterations. Suppression of cofilin function prevents spine loss, deficits in hippocampal synaptic plasticity, and impairments in long-term memory caused by sleep deprivation. The elevated cofilin activity is caused by cAMP-degrading phosphodiesterase-4A5 (PDE4A5), which hampers cAMP-PKA-LIMK signaling. Attenuating PDE4A5 function prevents changes in cAMP-PKA-LIMK-cofilin signaling and cognitive deficits associated with sleep deprivation. Our work demonstrates the necessity of an intact cAMP-PDE4-PKA-LIMK-cofilin activation-signaling pathway for sleep deprivation-induced memory disruption and reduction in hippocampal spine density. eLife digest The demands of modern society means that millions of people do not get sufficient sleep on a daily basis. Sleep deprivation, even if only for brief periods, can impair learning and memory. In many cases, this impairment appears to be related to changes in the activity of a brain region called the hippocampus. However, the exact processes responsible for producing the effects of sleep deprivation remain unclear. During learning or forming a new memory, the connections between the relevant neurons in the brain change. Havekes et al. found that depriving mice of sleep for just five hours dramatically reduced the connectivity between neurons in the hippocampus. This reduction is caused by the increased activity of cofilin, a protein that breaks down the actin filaments that shape the connections between neurons. Havekes et al. then used a virus to introduce an inactive version of cofilin into hippocampal neurons to suppress the activity of the naturally present cofilin. This manipulation prevented both the loss of the connections between neurons and the memory deficits normally associated with sleep deprivation. Havekes et al. also found that recovery sleep leads to the re-wiring of neurons in the hippocampus. [SEP] Justify:

Output: The text corroborates the claim by providing detailed evidence that sleep deprivation negatively impacts cognitive functions specifically related to memory and learning. It describes how periods of sleep loss lead to impaired memory consolidation and altered synaptic connectivity, particularly in the hippocampus, which is essential for learning. The findings illustrate that sleep deprivation can lead to significant reductions in dendritic spine numbers and disrupts the neural pathways necessary for memory formation, thus supporting the assertion that sleep deprivation impairs cognitive abilities.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: We limited BCa to a maximum value of 1 when Va ≥ Ra, which is a common assumption in assessing the relative quality of habitat or biodiversity [14]. While an attribute could continue to accrue above the reference value this may or may not represent an actual “gain” in biodiversity value. For each biodiversity attribute the averted loss, management gain and total benefit of the proposed offset can then be estimated relative to the reference on a scale of 0–1. For each vegetation attribute, in a specific scenario, we combined estimates of BC across all experts. We did this by firstly taking 80–85 random draws from each expert's elicited probability distributions for the reference state, future with offset and future under business as usual (if estimated for that starting scenario). For each attribute we then estimated BC for the starting state (BCstart), business as usual (BCbau) and future with offset (BCoffset) and also calculated averted loss (AL = BCstart—BCbau), management gain (MG = BCoffset-BCstart) and total benefit (TB = AL + MG) for each of the randomly drawn samples. Individual expert random samples of AL, MG and TB were then pooled, resulting in approximately 2100 randomly sampled estimates for each vegetation condition attribute that considered variation within and among each expert. Using these data, we explored, for a moderate condition WSGW, the distribution of AL, MG and TB for each vegetation attribute, except fern richness and cover. Ferns were excluded because they were often absent or of very low cover in expert reference distributions. We graphically explored variation in AL and MG for each attribute among individual experts and examined whether individual experts tended to be generally optimistic or pessimistic about future attribute trends, or whether their opinions differed depending on the attribute and management scenario (BAU or managed as an offset). We finally examined the distribution of MG in relation to BCstart for each attribute, except fern cover and richness, among WSGWs with differing attribute starting values (Table 2, S3 File). These starting values approximated the authors interpretations of a WSGW in a remnant, lightly or ungrazed, unfertilized state; a WSGW in a moderately grazed state; and a heavily grazed and fertilized WSGW. aggregate measure of biodiversity value, vegetation condition Initial vegetation condition. [SEP] Justify:

Output: The text contrasts the claim by discussing limitations in assessing the efficacy of biodiversity offsetting. It suggests that while it may be possible to estimate relative gains in biodiversity, these gains do not guarantee a full restoration of habitats to their original condition. The mention of whether attributes represent an actual 'gain' in biodiversity value indicates skepticism about the claim that offsets can fully restore lost habitats.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: 5 m s−1, where solid indicates southwesterlies and dashed indicates northwesterlies) along the direction from northwest to southeast averaged as in Figs. 1c and 4 diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The red solid curves show the averaged normalized diurnal precipitation deviation, with the red dashed straight line as the zero value. Analysis fields below the ground are masked with light-gray shadings. Citation: Review 138, 4; 10. 1175/2010MWR3356. 1Download FigureDownload figure as PowerPoint slide this content Copy this link, or click below to email it to a friend Email this content or copy the link directly: The link was not copied. Your current browser may not support copying via this button. Link copied successfullyCopy link Share on facebook Share on linkedin Share on twitter Abstract1. Introduction2. Precipitation dataset and mean3. The diurnal precipitation cycle and its phase propagation4. Discussion on diurnal propagation and nocturnal precipitation peaks5. Conclusions 850. Bonner, W. D. , 1968: Climatology of the low-level jet. Mon. Wea. Rev. , 96, 833– 850. )| falseSearch 4146. Carbone, R. E. , and J. D. Tuttle 2008: Rainfall occurrence in the U. S. warm season: The diurnal cycle. J. Climate, 21, 4132– 4146. )| falseSearch 2056. Carbone, R. E. , J. D. Tuttle D. Ahijevych and S. B. Trier 2002: Inferences of predictability associated with warm season precipitation episodes. J. Atmos. Sci. , 59, 2033– 2056. )| falseSearch 3103. doi:10. 1029/2008JD011103. Chen, G. , W. Sha and T. Iwasaki 2009: Diurnal variation of precipitation over southeastern China: Spatial distribution and its seasonality. J. Geophys. Res. , 114, D13103. doi:10. 1029/2008JD011103. )| falseSearch 6402. Dai, A. , F. Giorgi and K. E. [SEP] Justify:

Output: The text is largely technical data concerning diurnal precipitation cycles and does not address the concept of microclimates or variations in weather over short distances. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Future studies are now needed to determine how the neurons are able to re-wire themselves during recovery sleep. is a ubiquitous phenomenon and most species, including humans, spend a significant time asleep. Although the function of sleep remains unknown, it is widely acknowledged that sleep is crucial for proper brain function. Indeed, learning and memory, particularly those types mediated by the hippocampus, are promoted by sleep and disrupted by sleep deprivation (Havekes et al. , 2012a; Abel et al. , 2013; Whitney and Hinson, 2010). Despite the general consensus that sleep deprivation impairs hippocampal function, the molecular signaling complexes and cellular circuits by which sleep deprivation leads to cognitive deficits remain to be defined. The alternation of wakefulness and sleep has a profound impact on synaptic function, with changes observed in synaptic plasticity and transmission (Havekes et al. , 2012a; Abel et al. , 2013; Tononi and Cirelli, 2014). This relationship has led to the development of influential theories on the function of sleep (Tononi and Cirelli, 2006; Pavlides and Winson, 1989). Recent imaging suggests that dendritic structure is dynamic, especially during development, with alterations in spine numbers correlating with changes in sleep/wake state (Maret et al. , 2011; Yang and Gan, 2012). However, the impact of sleep deprivation or sleep on synaptic structure in the hippocampus in the context of memory storage or synaptic plasticity has not been examined. This is an important issue, as such structural changes in ensembles of synapses have been shown to play a critical role in memory storage (Caroni et al. , 2012; Vogel-Ciernia et al. , 2013). The formation of associative memories increases the number of dendritic spines in area CA1 of the hippocampus (Leuner et al. , 2003). Also, the induction of long-term potentiation (LTP), a cellular correlate of memory storage (Mayford et al. , 2012), is associated with an increase in spine density in cultured hippocampal neurons (Oe et al. , 2013). In addition to a critical function during development (Gurniak et al. , 2005), cofilin plays an essential role in synapse structure by mediating both the enlargement and pruning of dendritic spines (Rust, 2015; Bamburg, 1999; Bosch et al. , 2014). [SEP] Justify:

Output: The text corroborates the claim by mentioning that sleep deprivation disrupts learning and memory, particularly those types mediated by the hippocampus. It states that the general consensus is that sleep deprivation impairs hippocampal function, which directly relates to cognitive deficits, supporting the argument that sleep deprivation negatively impacts cognitive functions.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: While there was variation in expert opinion of the aggregate value to biodiversity of each vegetation patch, rankings and aggregate scores were relatively consistent among the 29 experts (Fig 2A and 2E). A boosted regression tree of initial vegetation condition revealed that expert identity had the largest relative influence (a normalised relative influence, NRI, of 31) such that some experts tended to score all sites higher or lower than the average expert. Shrub cover (NRI = 22), grass cover (NRI = 20) and tree cover (NRI = 10) were also found to be relatively important in explaining expert judgements of vegetation condition. Expert estimates of vegetation condition tended to be positively correlated with all native vegetation attributes but negatively correlated with invasive alien plant cover (S4 File). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 2. Boxplots of expert estimates of vegetation condition (A, E), management gain (B, F), averted loss (C, G) and total benefit (D, H). A through D are the five common vegetation patches provided to all 29 experts. Each patch in E through H was assessed by 4–6 experts. In all graphs the sites are ranked according to panel E, i. e. by their median current expert vegetation condition score. Averted loss and total benefit are based on estimates from 16 experts. Future predictions of biodiversity value. Most experts predicted that MG would be relatively small (Fig 2B & 2F). Median expert estimates of MG across the 64 patches was 5 100, with lower 25th and upper 75th quartiles of 0 and 10 respectively. However, a single expert had median predictions of 25 100 and a maximum of 70100 (S5 File) while negative estimates of MG were also elicited from some experts (Fig 2B & 2F). The boosted regression tree models indicated that expert identity had the largest relative influence on management gain (NRI = 72), with only small contributions from initial vegetation condition (NRI = 15. 4), invasive alien plant cover (NRI = 2. 9) and landscape vegetation cover (NRI = 2. 5). The data (Fig 2B & 2F) and BRT fitted functions (S4 File) suggest a negative, yet non-linear correlation between current biodiversity value and MG, a weak positive effect of landscape vegetation cover and a negative effect of invasive alien plant cover. [SEP] Justify:

Output: The text contrasts the claim by indicating that expert estimates of management gain (MG) are relatively small, with a median prediction of only 5 100 across the evaluated patches, which suggests limited restoration potential. Additionally, some experts provided negative estimates of management gain, further implying that biodiversity offsetting does not fully restore lost habitats to their original condition.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Trenberth 1999: Observed and model simulated precipitation diurnal cycle over the contiguous United States. J. Geophys. Res. , 104, 6377– 6402. )| falseSearch 744. Dai, A. , X. Lin and K-L. Hsu 2007: The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes. Climate Dyn. , 29, 727– 744. )| falseSearch 507. Higgins, R. W. , Y. Yao E. S. Yarosh J. E. Janowiak and K. C. Mo 1997: Influence of the low-level jet on summertime precipitation and moisture transport over the central United States. J. Climate, 10, 481– 507. )| falseSearch 3105. doi:10. 1029/2005JD006156. Janowiak, J. E. , V. E. Kousky and R. J. Joyce 2005: Diurnal cycle of precipitation determined from the high spatial and temporal resolution global precipitation analyses. J. Geophys. Res. , 110, D23105. doi:10. 1029/2005JD006156. )| falseSearch 503. Joyce, R. J. , J. E. Janowiak P. A. Arkin and P. Xie 2004: : A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor. , 5, 487– 503. )| falseSearch 933. Koch, S. E. , F. Zhang M. L. Kaplan Y-L. Lin R. Weglarz and C. M. Trexler 2001: Numerical simulations of a gravity wave event over CCOPE. Part III: The role of a mountain–plains solenoid in the generation of the second wave episode. Mon. Wea. Rev. , 129, 909– 933. )| falseSearch 2415. Lin, Y. , and F. Zhang 2008: Tracking mesoscale gravity waves in baroclinic jet-front systems. J. Atmos. Sci. , 65, 2402– 2415. )| falseSearch 168. Qian, W. , H. Kang and D. [SEP] Justify:

Output: The text does not address the claim regarding microclimates and their ability to create weather variations over short distances. Instead, it consists of a list of references related to precipitation and meteorological studies without connecting to the concept of microclimates specifically. Therefore, it is deemed irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: The activity of cofilin is negatively regulated by phosphorylation. Specifically, phosphorylation of serine 3 of cofilin suppresses its depolymerizing and F-actin severing activity (Bamburg, 1999). Importantly, increased cofilin activity can lead to the depolymerization and severing of F-actin, which in turn results in the shrinkage and loss of spines (Rust, 2015; Zhou et al. , 2004; Davis et al. , 2011; Shankar et al. , 2007). Hippocampal cofilin phosphorylation levels are increased after the induction of long-term potentiation (LTP) (Rex et al. , 2010; Chen et al. , 2007; Briz et al. , 2015), and during memory consolidation (Fedulov et al. , 2007; Suzuki et al. , 2011). Additionally, elevated cofilin activity in the hippocampus was recently implicated in abnormal spine structure and function in mutant mice with altered chromatin remodeling (Vogel-Ciernia et al. , 2013). Here we show for the first time that 5 hr of sleep deprivation leads to the loss of dendritic spines of CA1, but not CA3, neurons in the dorsal hippocampus. The spine loss in CA1 neurons was accompanied by reductions in dendrite length. This process was readily reversed by sleep, with just 3 hr of recovery sleep normalizing this spine loss and dendrite length. The molecular mechanisms underlying these negative effects of sleep deprivation were shown to target cofilin, whose elevated activity could contribute to spine loss. Indeed, suppression of cofilin activity in hippocampal neurons prevented the structural, biochemical, and electrophysiological changes as well as the cognitive impairments associated with sleep loss. The elevated cofilin activity is caused by the activity of the cAMP degrading phosphodiesterase-4A5 isoform (PDE4A5), which suppresses activity of the cAMP-PKA-LIMK pathway. Genetic inhibition of the PDE4A5 isoform in hippocampal neurons restores LIMK and cofilin phosphorylation levels and prevents the cognitive impairments associated with sleep loss. Thus changes in the cAMP-PDE4-PKA-LIMK-cofilin signaling pathway in the adult hippocampus underlie the cognitive deficits associated with sleep loss. [SEP] Justify:

Output: The text corroborates the claim by detailing how sleep deprivation leads to cognitive impairments, including structural and biochemical changes in the hippocampus that affect cognitive functions. It explicitly states that 'suppression of cofilin activity in hippocampal neurons prevented the structural, biochemical, and electrophysiological changes as well as the cognitive impairments associated with sleep loss,' thus linking sleep deprivation to cognitive deficits.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Averted loss (AL), derived from expert estimates of the change in vegetation condition under a BAU scenario, was estimated by 16 experts (Fig 2C & 2G). The median estimate of AL was 5, with upper and lower quartiles of 0 and 9 respectively under a BAU scenario. A single expert predicted that vegetation condition would weakly increase with BAU. Experts again were consistently optimistic or pessimistic in their estimate of averted loss (NRI = 60. 3). Initial vegetation condition (NRI = 13. 0), invasive alien plant cover (NRI = 13. 2) and landscape vegetation cover (NRI = 4. 2) each had only small to moderate relative influence in the BRT. Data and fitted functions from the BRT indicate a weak positive non-linear relationship between current biodiversity value and expert estimates of AL. Averted loss was greatest at moderate to high initial vegetation condition (Fig 2C & 2G, S4 File). Averted loss was also weakly negatively correlated with landscape vegetation cover and positively with HT weed cover. Total benefit (TB) was estimated from values elicited from 16 experts. Median TB was 7. 5 with lower (25th) and upper (75th) quartiles of 5 and 15 respectively. There was large among-expert variation in TB, evident in the normalised relative influence from the BRT (NRI = 66. 3). Moderate relative influence was also associated with initial vegetation condition (NRI = 21. 9) and a slight influence of invasive alien plant cover (NRI = 4. 9). Data (Fig 2D & 2H) and the fitted functions from the BRT suggested that TB tended to be highest when initial vegetation condition was low to moderate (S4 File). There was little support for any correlation with either invasive alien plant cover or landscape vegetation cover (S4 File). The lack of effect of invasive alien plant cover or landscape vegetation cover was presumably because the magnitudes of AL and MG were similar and each was associated with opposing effects (ie. MG = +ve landscape vegetation cover, -ve invasive alien plant cover; AL = -ve landscape vegetation cover, +ve invasive alien plant cover). Individual attribute predictions from the trial roulette method Averted loss, management gain and total benefit for a WSGW. [SEP] Justify:

Output: The text contrasts the claim by implying limitations in biodiversity offsetting through the description of averted loss (AL) and management gain (MG). The variability in expert estimates and the weak correlations observed in biodiversity value and averted loss suggest that fully restoring lost habitat to its original condition is challenging, as indicated by the details surrounding initial vegetation condition and invasive alien plant cover, which do not support the idea of complete restoration.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Lee 2002: Distribution of seasonal rainfall in Monsoon region. Theor. Appl. Climatol. , 73, 151– 168. )| falseSearch 2461. Trier, S. B. , C. A. Davis D. A. Ahijevych M. L. Weisman and G. H. Bryan 2006: Mechanisms supporting long-lived episodes of propagating nocturnal convection within a 7-day WRF model simulation. J. Atmos. Sci. , 63, 2437– 2461. )| falseSearch 328. Tripoli, G. J. , and W. R. Cotton 1989: Numerical study of an observed orogenic mesoscale convective system. Part II: Analysis of governing dynamics. Mon. Wea. Rev. , 117, 305– 328. )| falseSearch 419. Wallace, J. M. , 1975: Diurnal variations in precipitation and thunderstorm frequency over the conterminous United States. Mon. Wea. Rev. , 103, 406– 419. )| falseSearch 1629. Wang, C. C. , G. T. J. Chen and R. E. Carbone 2004: A climatology of warm-season cloud patterns over east Asia based on GMS infrared brightness temperature observations. Mon. Wea. Rev. , 132, 1606– 1629. )| falseSearch 1508. Wolyn, P. G. , and T. B. McKee 1994: The mountain–plains circulation east of a 2-km-high north–south barrier. Mon. Wea. Rev. , 122, 1490– 1508. )| falseSearch 1170. Yin, S. , D. Chen and Y. Xie 2009: Diurnal variations of precipitation during the warm season over China. Int. J. Climatol. , 29, 1154– 1170. )| falseSearch 1704. doi:10. 1029/2006GL028129. Yu, R. , T. Zhou and A. Xiong 2007: Diurnal variation of summer precipitation over contiguous China. Geophys. Res. Lett. , 34, L01704. doi:10. 1029/2006GL028129. )| falseSearch 2796. Zhang, F. , and S. E. [SEP] Justify:

Output: The text provided consists of references to various studies and articles relating to meteorology and climatology, but it does not address the claim about microclimates and their ability to create variations in weather over short distances. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: These observations provide a molecular model for the notion that prolonged wakefulness reduces structural signaling and negatively impacts dendritic structure, which is then restored with sleep. deprivation causes a robust reduction in apical and basal CA1 spine numbers and dendrite length To determine whether short periods of sleep loss affect dendritic structure in the hippocampus, we used Golgi staining to examine the length of dendrites and number of dendritic spines in the mouse hippocampus following 5 hr of sleep deprivation, a period of sleep loss that is known to impair selectively hippocampus-dependent memory consolidation and synaptic plasticity (Havekes et al. , 2012a; Abel et al. , 2013; Graves et al. , 2003; Vecsey et al. , 2009; Havekes et al. , 2014). Analyses of Golgi-impregnated CA1 neurons (Figure 1A) indicated that sleep deprivation significantly reduced the apical/basal spine density (Figure 1B; spine numbers per dendrite, NSD: 1. 42 ± 0. 03, SD: 1. 17 ± 0. 02; Student’s t-test, p=0. 0002) and dendrite length (Figure 1C; NSD: 1198. 4 ± 31. 6, SD: 984. 2 ± 29. 8 µm; Student’s t-test, p=0. 0012). This decrease in spine density and dendrite length was observed in both apical and basal dendrites (Figure 1—figure supplement 1A,B). To complement our Golgi studies, we conducted an additional experiment in which individual CA1 neurons in hippocampal slices from sleep deprived and non-sleep deprived mice were labeled using the DiI method as described (Seabold et al. , 2010). In line with our Golgi studies, we found that sleep deprivation significantly reduced the total number of spines on apical dendrites of CA1 neurons (Figure 1D; NSD: 1. 0 ± 0. 03, SD: 0. 84 ± 0. 04 Student’s t-test, p=0. 033; Figure 1E; NSD: 1. 0 ± 0. 06, SD: 0. 86 ± 0. 02 Student’s t-test, p=0. 03). [SEP] Justify:

Output: The text corroborates the claim by providing detailed evidence that sleep deprivation negatively impacts dendritic structure and spine density in the hippocampus, areas critical for memory consolidation and synaptic plasticity, which are essential components of cognitive function. The findings, supported by statistical analysis, indicate that sleep deprivation significantly reduces both the length of dendrites and the density of dendritic spines. This aligns with the claim that sleep deprivation impairs cognitive functions.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: For each vegetation condition attribute, we estimated the distribution of AL, MG and TB by randomly sampling from each expert’s reference, future under BAU and future with offset probability distributions. These random samples were pooled (2100 random samples for each attribute) and the resultant distributions of AL, MG and TB reflect both variation within and among experts (Fig 3, S6 File). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 3. Boxplots of averted loss, management gain and total benefit after 20 years for individual vegetation attributes within a with moderate starting values. For each attribute, data are derived by pooling 2100 random draws from the future and reference probability distributions of 25 experts. Total benefit is the sum of averted loss and management gain. Averted loss is estimated as the difference between the counterfactual and the initial attribute condition and therefore takes a positive value when the attribute is expected to decline relative to the reference under a business as usual scenario. Ltrees = Large Trees. lower quartile (25th percentile) of AL estimates were >0 for the richness of all growth forms, the foliage cover of grasses and trees and the number of large trees (Fig 3). This suggests a tendency towards a consensus that these vegetation condition attributes will decline under a BAU scenario. Predictions of AL were less consistent for forb, shrub, length of fallen logs and litter cover (Fig 3). Positive estimates of MG indicate an expert opinion that with an offset the attribute is expected to improve within 20 years, relative to the starting value. Both litter cover and the length of fallen logs were generally expected to increase following adoption of an offset (lower quartile is >0) while declines, or at best no change, in large trees numbers was thought to be most likely (Fig 3). Although some other attributes tended to have a median MG > 0 (e. g. tree and forb foliage cover and grass richness), predictions were uncertain owing to both inter- and intra-variation among experts (S6 File) and both declines and increases were predicted. In all cases, median predictions of absolute changes with management were never more than one species or 5% foliage cover. The distribution of TB indicated that the sum of MG and AL were predicted to be mostly positive, except for large trees and tree richness (Fig 3). In most cases TB was dominated by AL. [SEP] Justify:

Output: The text contrasts the claim by discussing various attributes of vegetation condition and the predictions regarding averted loss (AL) and management gain (MG) in the context of biodiversity offsetting. It indicates that while some vegetation attributes may improve with offsetting (positive MG), there are also considerable uncertainties and declines expected in certain attributes, such as the number of large trees. Specifically, the text mentions that 'predictions were uncertain' and that 'mediocre changes with management were never more than one species or 5% foliage cover,' which suggests that biodiversity offsetting may not fully restore lost habitat to its original condition.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Koch 2000: Numerical simulation of a gravity wave event observed during CCOPE. Part II: Wave generation by an orographic density current. Mon. Wea. Rev. , 128, 2777– 2796. )| falseSearch 4010. Zhou, T. , R. Yu H. Chen A. Dai and Y. Pan 2008: Summer precipitation frequency, intensity, and diurnal cycle over China: A comparison of satellite data with rain gauge observations. J. Climate, 21, 3997– 4010. )| falseSearch 1. (a) Study domain map and terrain distribution (shaded every 200 m) over northern China with the focus area highlighted in box ABCD, which includes the cities of Beijing (denoted as BJ) and Tianjin (TJ) and the provinces of Shanxi (SX), Inner-Mongolia (NM), Hebei (HB), Shandong (SD), Henan (HN), Anhui (AH), and Jiangsu (JS). (b) Distribution of mean warm-season hourly rainfall rate (mm h−1) averaged over all hours from May to August during 2003–09 estimated with the dataset. (c) Averaged terrain elevation and hourly precipitation rate along the northwest–southeast direction averaged over the boxed area in (a). The starting point of the cross section is an average over line AB, and the ending point is an average over line CD; the same averaging method will be used for Figs. 4 and 6 along the same cross section. View in gallery Fig. 2. (a) Percentage of the diurnal contributions of the total precipitation (calculated as the sum of the absolute differences between the mean precipitation rate at each hour and the mean hourly precipitation rate divided by the mean hourly precipitation rate). The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. (b) Isochrones (phase fronts) of the local diurnal precipitation peaks at different times of Fig. 3 with terrain elevations shaded every 200 m. The time label of the isochrones in (b) is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View in gallery Fig. 3. [SEP] Justify:

Output: The text provided does not address the claim regarding microclimates and their effects on weather variations. Instead, it contains references to particular studies and data concerning rainfall and terrain elevation in northern China, but lacks any discussion or evidence about microclimates or their influence on local weather patterns. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Figure 1 with 2 supplements see all Download asset Open asset Sleep deprivation reduces spine numbers and dendrite length in CA1 neurons of the hippocampus. (A) Representative images of Golgi-impregnated dendritic spines of CA1 pyramidal neurons from sleep deprived (SD) and non-sleep deprived (NSD) mice. Scale bar, 5 µm. (B) Sleep deprivation reduces the spine density of apical/basal dendrites of CA1 neurons (n = 5–6, Student’s t-test, p=0. 0002). (C) Sleep deprivation decreases apical/basal dendrite length of CA1 neurons (n = 5–6, Student’s t-test, p=0. 0012). (D, E) Comparative analyses of spine numbers in the second-third branch of apical dendrites of CA1 neurons reveal a significant reduction as a result of sleep deprivation using either the DiI labeling method (n = 3–4, Student’s t-test, p=0. 03) or Golgi analyses (n = 5, Student’s t-test, p=0. 03). Importantly, for the comparison of the two methods we focused on the second and third branch of the apical dendrites. See also the Materials and methods section. (F) Sleep deprivation reduces the number of all spine types in apical/basal dendrites of CA1 neurons (n = 5–6, Student’s t-test, p<0. 005). (G) Sleep deprivation reduces spine density of apical/basal dendrites between 60 and 150 µm away from the soma of CA1 neurons (n = 5–6, Student’s t-test, p<0. 005). (H) Sleep deprivation reduces apical/basal spine density in branch 3–9 of CA1 neurons (n = 5–6, Student’s t-test, p<0. 005). NSD: non-sleep deprived, SD: sleep deprived, Values represent the mean ± SEM. *p<0. 05, ***p<0. 005, by Student’s t test. See also Figure 1—figure supplement 1 and 2 for separate Golgi analyses of apical and basal spine numbers. [SEP] Justify:

Output: The text provides evidence that sleep deprivation reduces spine numbers and dendrite length in CA1 neurons of the hippocampus, which is relevant to cognitive functions since the hippocampus plays a critical role in learning and memory. It describes various analyses showing significant reductions in dendritic structures following sleep deprivation, implying a potential mechanism for cognitive impairment linked to attention and learning ability.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: However, the results also indicated that TB can also often be negative, indicative of an expectation that even with an offset the attribute might decline relative to the counterfactual. Across all experts and all attributes, 25% of all randomly drawn estimates of TB were negative and a further 15% were 0, indicating no expected benefit. Variation among individual experts was large, although most experts tended to be pessimistic about the future trends for large trees and optimistic about trends for logs and litter (Fig 4), regardless of the management scenario. There was some tendency to suggest that those experts who were pessimistic about BAU trends also tended to be pessimistic with an offset (Fig 4). This pattern was evident as a negative correlation between expert average estimates of averted loss and management gain, and was most apparent for logs, shrub foliage cover and tree species richness. Although some experts demonstrated a systematic bias (either consistently optimistic or pessimistic), most were not and their opinions differed among vegetation attributes (S7 File). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 4. Individual expert average estimates of averted loss and management gain for each of 11 vegetation attributes, with relationships indicated by simple linear fits. Each point represents the mean opinion of an individual expert, derived from their elicited probability distributions. Estimates in the top left quadrant of each panel indicate an optimistic opinion about future trends under both BAU and an offset (i. e. low AL, high MG). Estimates in the bottom right quadrat indicate those where experts were pessimistic about future trends, regardless of management scenario (i. e. high AL, low MG). Those in the top right were pessimistic about trends under BAU but optimistic about trends with an offset (i. e. high AL, high MG). Do management gains vary according to attribute and site starting conditions? The distribution of management gains for each attribute were explored across three WSGW scenarios. The three scenarios had different initial attribute values, and were generally thought to represent good-, moderate- and poor-quality sites (Table 2). These broad site quality descriptions were supported by predictions of aggregate vegetation condition, out of 100, derived from an ensemble of expert BRT models (see S8 File). We examined how MG varied depending on the initial condition (BCstart) of each vegetation attribute (initial value relative to the reference value) and whether consistent patterns were evident in each of the three WSGW vegetation patches. [SEP] Justify:

Output: The text contrasts the claim by highlighting that even with biodiversity offsetting, there are negative expectations regarding the restoration of habitat attributes. It notes that a significant portion of experts (25%) presented negative estimates for expected biodiversity returns, indicating that offsetting might not fully restore habitats. The evidence points to a pessimistic expectation about the effectiveness of offsets in delivering the anticipated management gains.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Map distribution of normalized diurnal precipitation deviations at (a) 0600, (b) 0900, (c) 1200, (d) 1500, (e) 1800, and (f) 0000 UTC estimated as the mean precipitation rate at this hour minus the mean precipitation rate throughout the date, normalized by the standard deviation of hourly precipitation at each location. Scales smaller than 200 km are truncated by a 2D spectral decomposition. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima). View in gallery Fig. 4. Distance–time Hovmöller diagram of the normalized hourly precipitation deviation without filtering averaged along the cross section from northwest to southeast (as in Fig. 1c). The dashed arrows show the southeastward phase propagation of the diurnal precipitation peaks. The time axis is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View in gallery Fig. 5. Spatial distribution of the 900-hPa vertical motions (colored) and perturbation wind vectors diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima) as in Fig. 3. View in gallery Fig. 6. Vertical profiles of the averaged vertical motion (colored; cm s−1), the perturbation vertical circulation vectors (horizontal wind component along the cross section and 100 times the vertical velocity), and the horizontal wind component perpendicular to the cross section (contoured in blue every 0. 5 m s−1, where solid indicates southwesterlies and dashed indicates northwesterlies) along the direction from northwest to southeast averaged as in Figs. 1c and 4 diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The red solid curves show the averaged normalized diurnal precipitation deviation, with the red dashed straight line as the zero value. [SEP] Justify:

Output: The text does not provide relevant information to support or contrast the claim about microclimates affecting weather variations over short distances. It discusses precipitation deviations and their measurements in detail but does not relate these data to microclimates or their effects on weather variability.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: Subtype-specific apical/basal spine analyses of the Golgi impregnated neurons revealed a significant decrease for all spine subtypes in sleep-deprived mice (Figure 1F, for all spine types, Student’s t-tests p<0. 005, for separate apical and basal spine analyses see 1C,D). Sleep deprivation causes the greatest reduction in apical/basal spine density between 60 µm and 150 µm from the soma (Figure 1G, for separate apical and basal spine analyses see Figure 1—figure supplement 1E,F). This region corresponds to the middle range of the dendritic branch (third to ninth branch orders, Figure 1H) where the primary input from CA3 is located (Neves et al. , 2008), suggesting that the hippocampal Schaffer collateral pathway is particularly vulnerable to sleep loss. We next assessed whether sleep deprivation also impacted spine numbers and dendrite length of CA3 neurons. Surprisingly, in contrast to CA1 neurons, CA3 neurons were unaffected by sleep deprivation. We did not observe reductions in spine density or dendrite length of either basal or apical dendrites of any type (Figure 1—figure supplement 2). Together, these data suggest that CA1 neurons at the level of dendritic structure seem particularly vulnerable to sleep deprivation. To determine whether recovery sleep would reverse spine loss in CA1 neurons, we repeated the sleep deprivation experiment but then left the sleep-deprived mice undisturbed for three hours afterwards. This period was chosen as our previous work indicated that three hours of recovery sleep is sufficient to restore deficits in LTP caused by sleep deprivation (Vecsey et al. , 2009). In line with the electrophysiological studies, recovery sleep restored apical/basal spine numbers and dendrite length in CA1 neurons to those observed in non-sleep deprived mice (Figure 2A, spine density of apical/basal dendrites, NSD: 1. 23 ± 0. 02, RS: 1. 29 ± 0. 02; Student’s t-test, p>0. 05; Figure 2B, dendrite length in μm, NSD: 1817. 0 ± 64. 6, RS: 1741. 6 ± 55. 57; Student’s t-test, p=0. [SEP] Justify:

Output: The text corroborates the claim by detailing experimental findings that indicate sleep deprivation significantly affects the structural integrity of neurons, particularly in the context of apical/basal spine density in CA1 neurons. It suggests that the hippocampal Schaffer collateral pathway is especially vulnerable to sleep loss, which ties into the broader assertion that sleep deprivation impairs cognitive functions, as these neuronal structures are crucial for processes like memory and learning.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: There was generally a negative relationship between the median expert estimate of MG and the attribute’s starting value relative to the reference value (Fig 5) and attributes whose starting condition were <0. 3 tended to increase with adoption of an offset. This same trend was observed in eli5. Median expert predictions of management gain and current condition for each of 11 vegetation attributes in poor-, moderate- and good-quality WSGW. Management gains are changes in the individual attributes, relative to a reference value, predicted to occur as a result of 20 years of offset management. Points are medians and lines are upper and lower quartiles derived from 2100 randomisations across 25 expert probability distributions. The broad qualitative site quality is supported by numeric estimates of aggregate vegetation condition, out of 100, derived from expert BRT models (condition score in legend in brackets). the good-quality scenario the median management gain was 0 for all attributes. With the exception of logs and litter cover, median initial condition was 1, indicating that 50% of the random samples were less than the starting attribute values. In such circumstances standardisation to a reference can obscure changes on the original measurement scale, especially if experts predicted an increase following adoption of an offset. However, median estimates of absolute changes on the original scale were small and never positive (S9 File). DiscussionBiodiversity offsetting has been widely adopted but we often lack adequate empirical data and models upon which to make predictions of future biodiversity outcomes. Predicting the likely ecological changes with and without offsets is fundamental to offsetting, and these predictions can have significant and very real biodiversity impacts. Despite this, making such predictions remains a major technical challenge [2] and a better understanding of the uncertainty around future conservation benefits is vitally important. Here, 29 ecological professionals were asked, using two separate approaches, to make predictions about future changes in vegetation composition and structure, as a surrogate for biodiversity, with and without the adoption of a biodiversity offset. The results revealed that experts on average expected averted loss, management gain and total benefits to be modest. Individual expert predictions were often highly uncertain, and this was compounded by variation among experts. Strikingly, our analyses uncovered the perception that declines in some vegetation condition attributes were also likely at offset sites, even with the adoption of conservation management. This finding necessitates approaches that accept that some offsets will fail to achieve estimated gains, and some will deliver beyond expectation. Reasonable outcomes can be achieved overall (i. [SEP] Justify:

Output: The text contrasts the claim by highlighting that despite the adoption of biodiversity offsetting, there are predictions of modest management gains and potential declines in vegetation condition attributes at offset sites. The analysis indicates that some offsets may fail to achieve the intended ecological improvements, stating that 'some offsets will fail to achieve estimated gains, and some will deliver beyond expectation.' This suggests that biodiversity offsetting does not guarantee the full restoration of lost habitats to their original conditions.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Analysis fields below the ground are masked with light-gray shadings. raw image Fig. 1. (a) Study domain map and terrain distribution (shaded every 200 m) over northern China with the focus area highlighted in box ABCD, which includes the cities of Beijing (denoted as BJ) and Tianjin (TJ) and the provinces of Shanxi (SX), Inner-Mongolia (NM), Hebei (HB), Shandong (SD), Henan (HN), Anhui (AH), and Jiangsu (JS). (b) Distribution of mean warm-season hourly rainfall rate (mm h−1) averaged over all hours from May to August during 2003–09 estimated with the dataset. (c) Averaged terrain elevation and hourly precipitation rate along the northwest–southeast direction averaged over the boxed area in (a). The starting point of the cross section is an average over line AB, and the ending point is an average over line CD; the same averaging method will be used for Figs. 4 and 6 along the same cross section. View raw image Fig. 2. (a) Percentage of the diurnal contributions of the total precipitation (calculated as the sum of the absolute differences between the mean precipitation rate at each hour and the mean hourly precipitation rate divided by the mean hourly precipitation rate). The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. (b) Isochrones (phase fronts) of the local diurnal precipitation peaks at different times of Fig. 3 with terrain elevations shaded every 200 m. The time label of the isochrones in (b) is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View raw image Fig. 3. Map distribution of normalized diurnal precipitation deviations at (a) 0600, (b) 0900, (c) 1200, (d) 1500, (e) 1800, and (f) 0000 UTC estimated as the mean precipitation rate at this hour minus the mean precipitation rate throughout the date, normalized by the standard deviation of hourly precipitation at each location. Scales smaller than 200 km are truncated by a 2D spectral decomposition. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. [SEP] Justify:

Output: The text does not address the claim about microclimates and their impact on weather variations. It mainly discusses terrain distribution, precipitation rates, and diurnal contributions to rainfall without mentioning microclimates or their role in creating weather variations over short distances.


Input: Task: corroborate the claim using the given text. Claim: Sleep deprivation has been shown to impair cognitive functions, including attention, decision-making, and learning ability. [SEP] Text: 1721; Figure 2C, Student’s t-test, p>0. 05 for each distance from soma; Figure 2D, Student’s t-test, p>0. 05 for each branch number; for separate apical and basal spine analyses see Figure 2—figure supplement 1) with the exception of branched spines in the basal dendrites (Figure 2—figure supplement 1C). Recovery sleep slightly but significantly elevated the number of filopodia spines of the apical CA1 dendrites and total spine numbers of the seventh and eighth branch of the apical and basal dendrites respectively (Figure 2—figure supplement 1). Figure 2 with 1 supplement see all Download asset Open asset Three hours of recovery sleep restores spine numbers and dendrite length of CA1 neurons in the hippocampus. (A) Golgi analyses indicated that three hours of recovery sleep after 5 hr of sleep deprivation restores the total number of spines per apical/basal dendrite of CA1 neurons (n = 6, Student’s t-test, p>0. 05). (B) Three hours of recovery sleep after 5 hr of sleep deprivation restores apical/basal dendrite length of CA1 neurons (n = 6, Student’s t-test, p=0. 173). (C, D) Three hours of recovery sleep restores apical/basal spine numbers at all distances from the soma (Student’s t-test, p>0. 05 for each distance from soma, C) and at each branch number (Student’s t-test, p>0. 05 for each branch number, C). NSD: non-sleep deprived, RS: Sleep deprivation + recovery sleep. Values represent the mean ± SEM. See also Figure 2—figure supplement 1 for separate Golgi analyses of apical and basal spine numbers. Sleep deprivation increases hippocampal cofilin activity and suppression of cofilin function prevents spine loss in CA1 neurons associated with the loss of sleep We hypothesized that the structural changes in the hippocampus following sleep deprivation might be related to increased activity of the actin-binding protein cofilin because increased cofilin activity can cause shrinkage and loss of dendritic spines through the depolymerization and severing of actin filaments (Zhou et al. , 2004; Davis et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the effects of sleep deprivation on hippocampal neurons, indicating that sleep deprivation leads to structural changes in the hippocampus, potentially affecting cognitive functions. It specifically mentions the activity of cofilin, which is linked to spine loss and loss of dendritic structure; these changes are relevant to cognitive functions such as learning and memory, hence supporting the claim that sleep deprivation impairs cognitive abilities.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: e. across many offset scenarios), but only if risks are managed appropriately. The opportunity therefore exists to minimise risk and maximise biodiversity outcomes through the identification and avoidance of high-risk offset sites. The modest estimates of total benefits suggest that offset schemes should adopt a precautionary approach and avoid optimistic estimates of either averted loss or management gain. Several authors have argued that offset schemes often rely on unrealistically high estimates of background rates of loss (i. e. averted loss) to meet NNL requirements [8, 17]. If actual background rates of loss are indeed less than predicted, then to meet NNL these offset schemes would need to increase their reliance on management gains, achieved through conservation actions that improve on current biodiversity values. This could shift emphasis in offset site selection from high quality sites where there is much to lose to low quality sites where the potential for improvement is greater. Prediction of management gains at offset sites is, however, also inherently risky, as revealed by the substantial variation within and among experts. One approach to managing the uncertainty of future conservation benefits is to limit trading to only those offsets that have already generated benefits [5]. The range of opinion and uncertainty described in this study lends support to the pursuit of policy and governance mechanisms that favour early or prior conservation benefits. However, the creation of prior benefits requires significant planning and investment and in practice even the often-cited wetland mitigation banks in the USA rarely provide full prior compensation [43]. In most cases, offsetting is likely to continue to rely on future benefits in which case it is imperative that monitoring and research focus on improving empirical data and models to better predict outcomes and reduce uncertainty around these predictions. When and where do experts believe conservation management will produce benefits? Although predicted benefits were small, the results from both elicitation methods suggest that experts believe that management actions to address threats and pressures can often be sufficient to negate counterfactual losses. However, the study highlights that expert predicted benefits are not likely to be equivalent among all vegetation attributes. This offers opportunities for policy makers to refine offset schemes but also highlights important considerations for monitoring and evaluating current offset outcomes. Point estimates of future aggregate biodiversity value suggested that experts believed management gains (MG) were most likely to balance counterfactual averted losses (AL) when sites were initially of moderate to low value, relative to a reference condition. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and uncertainties associated with biodiversity offset schemes. It emphasizes that offset schemes often rely on optimistic estimates and that actual outcomes can vary significantly. It notes that even well-cited examples like wetland mitigation banks rarely provide full compensation, suggesting that biodiversity offsetting may not be able to fully restore lost habitats to their original condition. The mention of risks, the need for significant planning, and the inherent uncertainties in predicting management gains further challenges the claim.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima). View raw image Fig. 4. Distance–time Hovmöller diagram of the normalized hourly precipitation deviation without filtering averaged along the cross section from northwest to southeast (as in Fig. 1c). The dashed arrows show the southeastward phase propagation of the diurnal precipitation peaks. The time axis is in UTC [i. e. , 8 h behind the local standard time (Beijing time)]. View raw image Fig. 5. Spatial distribution of the 900-hPa vertical motions (colored) and perturbation wind vectors diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The 800-m terrain elevation is plotted in thick gray curves denoting the approximate location of the steepest terrain slope. The purple solid (dashed) line denotes the location of the phase fronts of the local diurnal precipitation peaks (minima) as in Fig. 3. View raw image Fig. 6. Vertical profiles of the averaged vertical motion (colored; cm s−1), the perturbation vertical circulation vectors (horizontal wind component along the cross section and 100 times the vertical velocity), and the horizontal wind component perpendicular to the cross section (contoured in blue every 0. 5 m s−1, where solid indicates southwesterlies and dashed indicates northwesterlies) along the direction from northwest to southeast averaged as in Figs. 1c and 4 diagnosed with the GFS analyses at (a) 0600, (b) 1200, (c) 1800, and (d) 0000 UTC. The red solid curves show the averaged normalized diurnal precipitation deviation, with the red dashed straight line as the zero value. Analysis fields below the ground are masked with light-gray shadings. 30 Views 0 0 0 Views 1290 365 19 PDF Downloads 697 210 11 of Variability on the in Storage over the Tigris–Basin Authors: Li-and Guo-Evaluation of Performance of to Authors:. Yang, Ardeshir Ebtehaj, Sajad Vahedizade, and A 440-of in California from Authors: Ian M. Howard, David W. Stahle, Michael D. Dettinger, Cody Poulsen, F. Martin Ralph, Max C. [SEP] Justify:

Output: The text does not address the claim directly. It discusses precipitation patterns and vertical motions without making any assertion regarding microclimates or their effect on weather variations over short distances. Therefore, it is irrelevant to the claim.