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: (D–F) CofilinS3D expression was excluded from astrocytes in area CA1 as indicated by a lack of co-labeling (F) between viral cofilin (D) and GFAP expression (E). Scale bar, 100 µM. (G) Virally delivered cofilinS3D protein levels were approximately 75% (blue bar) of wild-type cofilin levels (green bar). Wild-type cofilin levels were not significantly affected by expression of cofilinS3D. An HA-tag antibody was used to detect the mutant inactive form of cofilin. (n = 4). (H) Hippocampal cofilinS3D expression prevents spine loss in apical/basal dendrites of CA1 neurons that is associated with sleep deprivation (n = 6, Student’s t-test, p>0. 05). (I) Hippocampal cofilinS3D expression prevents the decrease in apical/basal dendritic spine length in neurons of hippocampal that is caused by sleep deprivation (n = 6, Student’s t-test, p>0. 05). (J) Sleep deprivation does not alter the number of any spine type in apical/basal dendrites of CA1 neurons in the hippocampus of mice expressing cofilinS3D (n = 6, Student’s t-test, p>0. 05). (K) Sleep deprivation does not attenuate apical/basal spine density at any distance from the soma in mice expressing cofilinS3D (n = 6, Student’s t-test, p>0. 05). NSD: non-sleep deprived, SD: sleep deprived. Values represent the mean ± SEM. **p=0. 0090. Student’s t test. See also Figure 3—figure supplement 1. For separate analyses of apical and basal spine numbers see Figure 3—figure supplement 2. Figure 3—source data 1 Sleep deprivation reduces cofilin phosphorylation in the hippocampus. The data source file contains the relative optical density values (in arbitrary units) of the pcofilin and cofilin western blots for each individual animal of the non-sleep deprived (NSD) and sleep deprived (SD) group. [SEP] Justify:

Output: The text corroborates the claim by indicating that sleep deprivation is linked to neurobiological changes, specifically the phosphorylation status of cofilin in the hippocampus, which is critical for synaptic function and may influence cognitive processes. It discusses the prevention of spine loss and dendritic spine length reduction in neurons due to sleep deprivation, suggesting that sleep deprivation negatively impacts neural structures associated with 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: Likewise, the future probability distributions provided by experts of individual attributes suggested that positive MGs would most likely arise when attributes were initially low relative to their reference. Where attributes are already at or near reference values, for example in good-quality remnant vegetation, conservation benefits are only likely if AL is relatively large and management can maintain current values. If those sites support attributes that are expected to decline, even with conservation management in place (e. g. large trees), then total benefits will inevitably be zero (or less). This would suggest that, if management gain is the objective, then offsets should be established in areas of low to moderate condition with some potential to improve. Offsets are usually framed around the concept of NNL or Net Gain. The uncertainty surrounding the potential for gains, highlighted in the results we present here, emphasises the risks associated with loss of good-quality habitats. In most jurisdictions offsets are applied to residual impacts as part of the mitigation hierarchy, with priority given to avoidance and minimisation of impact, especially in the case of rare or high-quality habitats [44]. Despite this, situations do arise when high-quality habitat are impacted by human activities. If low to moderate condition habitats are selected as offsets because of their greater capacity for generating gains, this could lead to trading good-quality high-value habitat for larger areas of low-quality habitat, albeit habitat that has considerable potential to improve. However, this assumes that the loss of biodiversity in good-quality habitat is equivalent to a numerically similar improvement in low- or moderate-quality habitat, which need not be the case. While this can be partly addressed by nuances in the metrics (e. g. multipliers applied to rare habitats) or policy settings (e. g. regulations to prohibit clearing of rare or high-value habitats), ultimately, these decisions come down to human p10. 1371/journal. pone. 0216703. s001(PDF) S2 File. Management gains, do they vary according to vegetation type? . S3 File. Elicitation material and scenarios. S4 File. Fitted boosted regression tree functions for initial aggregate vegetation condition, management gain, averted loss and total benefit. S5 File. Boxplots of individual expert estimates of aggregate management gain. S6 File. Boxplots of individual expert probability distributions of MG, AL and TB. S7 File. Are individual experts consistently optimistic or pessimistic? [SEP] Justify:

Output: The text contrasts the claim by highlighting the limitations of biodiversity offsetting. It explains that conservation benefits may only arise if attributes are low relative to their reference values and points out scenarios where attributes are expected to decline despite conservation management. This suggests that offsets, especially when based on low-to-moderate condition habitats, may not equate to fully restoring lost habitat. The statement emphasizes that trading good-quality habitat for lower-quality alternatives may not result in equivalent ecological benefits, showcasing the complexities and uncertainties in achieving 'Net Gain' through offsetting.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: for the Bulletin of the of of Atmospheric and Journal of of of Journal of the and Forecasting Weather, Climate, and in Sign up Sign in Sign up for the Bulletin of the of of Atmospheric and Journal of of of Journal of the and Forecasting Weather, Climate, and 1. Introduction2. Methodology3. Results4. Concluding remarks 3555. Horel, J. D. , A. N. Hahmann, and J. E. Geisler, 1989: An investigation of the annual cycle of convective activity over the tropical Americas. J. Climate,2, 1388–1403. Jones, C. , 1990: An investigation of low-frequency variability of the large-scale circulation over South America. M. S. thesis, Department of Meteorology, University of Utah, 108 pp. ——, and J. D. Horel, 1991: High circulation and convective activity over (in Portuguese). Rev. Brasileira de Meteorologia,5-1, 379–387. ——, D. E. Waliser, J. E. Schemm, and W. K. Lau, 2000: Prediction skill of the Madden and in dynamical extended range weather forecasts. Climate Dyn. , in press. Kalnay, E. , and Coauthors, 1996: NCEP/NCAR 40-Project. Bull. Amer. Meteor. Soc. ,77, 437–471. Lenters, J. D. , and K. H. Cook, 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev. ,127, 409–431. Liebmann, B. , G. N. Kiladis, J. A. Marengo, T. Ambrizzi, J. D. Glick, 1999: Submonthly convective variability over and the Convergence Zone. J. Climate,12, 1877–1891. Madden, R. A. , and P. R. Julian, 1994: Observations of the 40–50 day tropical oscillation: A review. Mon. Wea. Rev. ,122, 814–837. Nogues-Paegle J. , K. Mo, and J. [SEP] Justify:

Output: The text is irrelevant to the claim as it provides no information or discussion regarding microclimates and their impact on variations in weather. Instead, it contains a series of citations and references related to various atmospheric studies without addressing the specific 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: We subsequently determined whether expression of the inactive cofilinS3D prevented the loss of dendritic spines in hippocampal area CA1 caused by sleep deprivation. Analyses of Golgi-impregnated hippocampal neurons in area CA1 indicated that in cofilinS3D expressing mice sleep deprivation no longer reduced the spine density of apical/basal dendrites (NSD: 1. 42 ± 0. 03; SD: 1. 34 ± 0. 03; Student’s t-test, p>0. 05 Figure 3H,J,K; for separate apical and basal spine analyses see Figure 3—figure supplement 2) with the exception of a small but statistically significant reduction in branched spines of apical and basal dendrites (Figure 3, C, D) and a decrease in number of spines on apical dendrites about 180 µm away from the soma (Figure 3—figure supplement 2E,F). Likewise, sleep deprivation no longer affected dendrite length (NSD: 1283. 0 ± 35. 95 µm, SD: 1250. 1 ± 41. 19 µm; Student’s t-test, p=0. 5612; Figure 3I, for separate apical and basal dendrite length analyses see Figure 3—figure supplement 2B). Together these data suggest that suppressing cofilin function in hippocampal neurons prevents the negative impact of sleep deprivation on spine loss and dendrite length of CA1 neurons. Suppressing cofilin function in hippocampal neurons prevents the impairments in memory and synaptic plasticity caused by brief periods of sleep deprivation As a next step, we sought to determine whether prevention of the increase in cofilin activity in sleep-deprived mice would not only protect against the reduction in spine numbers on CA1 dendrites but also the functional impairment at the behavioral level. The consolidation of object-place memory requires the hippocampus (Oliveira et al. , 2010; Florian et al. , 2011) and is sensitive to sleep deprivation (Havekes et al. , 2014; Florian et al. , 2011; Prince et al. , 2014). Therefore, we assessed whether cofilinS3D expression would prevent cognitive deficits caused by sleep deprivation in this task. [SEP] Justify:

Output: The text corroborates the claim by indicating that sleep deprivation impacts memory and synaptic plasticity, which relate directly to cognitive functions. It mentions that 'suppressing cofilin function in hippocampal neurons prevents the impairments in memory and synaptic plasticity caused by brief periods of sleep deprivation,' thus supporting the assertion that sleep deprivation negatively affects cognitive capabilities.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can fully restore any lost habitat to its original condition. [SEP] Text: Individual expert mean estimates of averted loss and management gain. S8 File. Boosted regression tree for predicting vegetation condition to new sites. S9 File. Predicted change in individual attribute values with offset on original measurement scale. 1. Biodiversity offsets and the challenge of achieving no net loss. Conserv Biol. 2013; 27: 1254–64. pmid:24033441 View 2. Taming a wicked problem: Resolving controversies in biodiversity offsetting. Bioscience. 2016; 66: 489–98. View 3. Spash CL. Bulldozing biodiversity: The economics of offsets and trading-in Nature. Biol Conserv. 2015; 192: 541–51. View 4. . Biodiversity offsetting in dynamic landscapes: Influence of regulatory context and counterfactual assumptions on achievement of no net loss. Biol Conserv. 2017; 206: 314–9. View 5. The biodiversity bank cannot be a lending bank. Conserv Lett. 2010; 3: 151–8. View 6. von Hase A, et al. A loss-gain calculator for biodiversity offsets and the circumstances in which no net loss is feasible. Conserv Lett. 2016; 9: 252–9. View 7. . Calculating the benefit of conservation actions. Conserv Lett. 2013; 6: 359–67. View 8. . Locking in loss: Baselines of decline in Australian biodiversity offset policies. Biol Conserv. 2015; 192: 504–12. View 9. -Gulland EJ. Importance of baseline specification in evaluating conservation interventions and achieving no net loss of biodiversity. Conserv Biol. 2014; 28: 799–809. pmid:24945031 View 10. -Gulland EJ. Biodiversity offsets in theory and practice. Oryx. 2013; 47: 369–80. View 11. Faustian bargains? Restoration realities in the context of biodiversity offset policies. Biol Conserv. 2012; 155: 141–8. View 12. . Is there any empirical support for biodiversity offset policy? Ecol Appl. 2014; 24: 617–32. pmid:24988764 View 13. Commonwealth of Australia. Act 1999 Policy. Canberra: Department of Sustainability, Environment, Water, Population and Communities, 2012. 14. . [SEP] Justify:

Output: The text cites various sources that challenge the claim by discussing the complexities and limitations of biodiversity offsetting, including issues related to achieving no net loss. For instance, it mentions works like 'Biodiversity offsets and the challenge of achieving no net loss,' highlighting that offsets cannot simply 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: Paegle, 1998: Predictability of the NCEP/NCAR reanalysis model during austral summer. Mon. Wea. Rev. ,126, 3135–3152. Schemm, J. E. , H. Van den Dool, and S. Saha, 1996: A Multi-at NCEP. Preprints 11th Conf. on. Meteor. Soc. , 47–49. in gallery Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View in gallery Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View in gallery Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View in gallery Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). View in gallery Fig. 5. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on various meteorological terms and models without discussing microclimates or variations in weather over short distances. It mentions aspects like mean outgoing longwave radiation and total standard deviation of geopotential height, but none directly relate to the claim about microclimates.


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: Mice virally expressing eGFP or cofilinS3D were trained in this task 3 weeks after viral infection and sleep deprived for 5 hr immediately after training or left undisturbed in the home cage. Upon testing for memory the next day, sleep-deprived mice expressing eGFP showed no preference for the relocated object indicating that brief sleep deprivation impaired the consolidation of object-place memory. In contrast, mice expressing cofilinS3Dshowed a strong preference for the displaced object despite sleep deprivation (e: 45. 2 ± 6. 4%, eGFP SD: 33. 4 ± 2. 0%, cofilinS3D NSD: 51. 9 ± 2. 9%, cofilinS3D SD: 53. 2 ± 4. 6%; Figure 4A). Figure 4 with 2 supplements see all Download asset Open asset Increased cofilin activity in the hippocampus mediates the memory and synaptic plasticity deficits associated with sleep deprivation. (A) Mice expressing eGFP or cofilinS3D were trained in the hippocampus-dependent object-place recognition task. Half of the groups were sleep deprived for 5 hr and all mice were tested 24 hr later. Hippocampal cofilinS3D expression prevents memory deficits caused by sleep deprivation (n = 9–10, two-way ANOVA, effect of virus F1,35 = 18. 567, p=0. 0001; effect of sleep deprivation F1,35 = 2. 975, p=0. 093; interaction effect F1,35 = 4. 567, p=0. 040; eGFP SD group versus other groups, p<0. 05). The dotted line indicates chance performance (33. 3%). (B, C) Following 5 hr of sleep deprivation, long-lasting LTP was induced in hippocampal slices by application of four 100 Hz trains, 1 s each, spaced 5 min apart to the Schaffer collateral pathway. Five hours of sleep deprivation impairs long-lasting LTP in slices from mice expressing eGFP (n = 6–7, two-way ANOVA, effect of virus F1,10 = 21. 685, p<0. 001). In contrast, virally delivered cofilinS3D prevents sleep deprivation-induced deficits (n = 5, two-way ANOVA, effect of virus F1,8 = 0. [SEP] Justify:

Output: The text corroborates the claim by demonstrating that sleep deprivation in mice impaired their memory and learning abilities. The study highlighted that sleep-deprived mice expressing eGFP failed to show a preference for a relocated object, indicating an impairment in object-place memory. This aligns with the claim that sleep deprivation negatively affects cognitive functions, specifically 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: A disaggregated biodiversity offset accounting model to improve estimation of ecological equivalency and no net loss. Biol Conserv. 2016; 204: 322–32. View 15. The development of the Australian environmental offsets policy: from theory to practice. Environ Conserv. 2015; 42: 306–14. View 16. Government Queensland. offsets Policy (Version 1. 6). Conservation and Biodiversity Programs, Department of Environment and Science, 2018. View 17. . Perverse incentives risk undermining biodiversity offset policies. J Appl Ecol. 2015; 52: 532–7. View 18. Burgman MA. Trusting judgements. How to get the best out of experts. Cambridge, United Kingdom: Press; 2016. 203 p. 19. Keith D. Ocean shores to desert dunes: the native vegetation of Wales and the ACT. Department of Environment and Conservation. 2004. View 20. Thackway R, Cresswell I. for Australia: a framework for establishing the national system of reserves, Version 4. 0. 1995; 88. View 21. Commonwealth of Australia. Protection and Act 1999, Act No. 91 of 1999. 1999. 22. . Coal and coal seam gas resource assessment for the Namoi subregion. Product 1. 2 for the Namoi subregion from the Assessment. Australia: Department of the Environment, Bureau of and Geoscience Australia, 2014. 23. Prober SM. Conservation of the grassy white box woodlands: Rangewide floristic variation and implications for reserve design. Bot. 1996; 44: 57–77. View 24. . Conservation of the grassy white box woodlands: relative contributions of size and disturbance to floristic composition and diversity of remnants. Bot. 1995; 43: 349–66. View 25. Moreno-. The true loss caused by biodiversity offsets. Biol Conserv. 2015; 192: 552–9. View 26. . Ecological integrity assessment as a metric of biodiversity: are we measuring what we say we are? Biodivers Conserv. 2016; 25: 1011–35. View 27. Quetier F, Lavorel S. Assessing ecological equivalence in biodiversity offset schemes: Key issues and solutions. Biol Conserv. 2011; 144: 2991–9. View 28. . Assessing the quality of native vegetation: the ‘habitat hectares’ approach. [SEP] Justify:

Output: The text contrasts with the claim as it discusses the complexities and limitations surrounding biodiversity offsetting. Specifically, it mentions the potential for 'perverse incentives' and the true losses associated with biodiversity offsets. The reference to the article titled 'The true loss caused by biodiversity offsets' suggests that offsetting may 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: Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View in gallery Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View in gallery Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. raw image Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). View raw image Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. View raw image Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. [SEP] Justify:

Output: The text does not address the claim about microclimates and their effect on weather variations over short distances. It mainly discusses intraseasonal variability and anomaly correlation of meteorological forecasts without mentioning microclimates or their potential impact.


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: 016, p>0. 902). NSD: non-sleep deprived, SD: sleep deprived. Values represent the mean ± SEM. *p<0. 05 by posthoc Dunnet’s test, **p<0. 01 by Student’s t test. See also Figure 4—figure supplement 1. Figure 4—source data 1 CofilinS3D expression prevents memory deficits in the object-location memory task caused by sleep deprivation. The data source file contains the object exploration times for the displaced (DO) and non-displaced objects (NDO1, NDO2) for all individual animals of each group. Download elife-13424-fig4-data1-v3. xlsx Expression of the mutant form of cofilin did not affect object exploration during training, exploration of an open field or zero maze indicating that anxiety levels were unaffected by expression of cofilinS3D in the hippocampus (Figure 4—figure supplement 1A–C). Moreover, using a behaviorally naïve cohort of mice, we found that cofilinS3D expression did not alter short-term object-place memory in the same task (Figure 4—figure supplement 1D). Together, these findings demonstrate that cofilinS3D expression specifically prevents the cognitive deficits caused by sleep deprivation. Although we can not rule out the possibility of off-target effects of the cofilinS3D mutant, we think that these are unlikely as expression of this mutant form of cofilin reversed the effects of sleep deprivation, restoring spine loss and memory to non-sleep deprived levels while not having an effect in non-sleep deprived mice. To further define the role of cofilin in impairments in hippocampal function caused by sleep deprivation, we next determined if suppression of cofilin activity would prevent the deficits in hippocampal LTP caused by brief periods of sleep deprivation (Havekes et al. , 2012a; Abel et al. , 2013; Vecsey et al. , 2009; Prince et al. , 2014). Five hours of sleep deprivation significantly impaired long-lasting LTP induced by 4 high-frequency trains of electrical stimuli applied at 5-minute intervals (spaced 4-train stimulation) in hippocampal slices from mice expressing eGFP (Figure 4B), confirming our previously published findings with non-injected wild-type mice (Vecsey et al. , 2009). [SEP] Justify:

Output: The text corroborates the claim by providing evidence that sleep deprivation significantly impairs cognitive functions, specifically in the context of memory and long-term potentiation (LTP) in the hippocampus. It discusses findings that demonstrate how cofilinS3D expression can prevent cognitive deficits caused by sleep deprivation, and highlights that five hours of sleep deprivation impaired long-lasting LTP induced by high-frequency stimulation, further confirming the adverse effects on cognitive abilities as stated in the claim.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: Biodiversity offsetting and conservation: reframing nature to save it Published online by Press: 06 October 2015 andWilliam M. author details Evangelia Apostolopoulou* Affiliation: Department of Geography, University of Cambridge, CB2 3. Adams Affiliation: Department of Geography, University of Cambridge, CB2 3EN, Cambridge, UK * (Corresponding author) E-mail ea367@cam. ac. uk 0. 19 mb) View PDF [Opens in a new window] Save to to Save to & Permissions [Opens in a new window] offsetting involves the balancing of biodiversity loss in one place (and at one time) by an equivalent biodiversity gain elsewhere (an outcome referred to as Loss). The conservation science literature has chiefly addressed the extent to which biodiversity offsets can serve as a conservation tool, focusing on the technical challenges of its implementation. However, offsetting has more profound implications than this technical approach suggests. In this paper we introduce the concept of policy frames, and use it to identify four ways in which non-human nature and its conservation are reframed by offsetting. Firstly, offsetting reframes nature in terms of isolated biodiversity units that can be simply defined, measured and exchanged across time and space to achieve equivalence between ecological losses and gains. Secondly, it reframes biodiversity as lacking locational specificity, ignoring broader dimensions of place and deepening a nature–culture and nature–society divide. Thirdly, it reframes conservation as an exchange of credits implying that the value of non-human nature can be set by price. Fourthly, it ties conservation to land development and economic growth, foreshadowing and bypassing an oppositional position. We conclude that by presenting offsetting as a technical issue, the problem of biodiversity loss due to development is depoliticized. As a result the possibility of opposing and challenging environmental destruction is foreclosed, and a dystopian future of continued biodiversity loss is presented as the only alternative. unitsconservation bankingconservation creditsmetricsmitigation hierarchyoffsetsoffsetting Issue 1 January 2017 10. 1017/S0030605315000782 [Opens in a new window] This is an 4. 0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and implications of biodiversity offsetting. It states that offsetting involves balancing biodiversity loss in one area by gaining biodiversity elsewhere, but it also highlights profound issues such as depoliticizing the problem of biodiversity loss and suggesting a dystopian future where biodiversity continues to decline. The text indicates that the approach can ignore broader ecological contexts and reduces conservation to a mere exchange of credits, thereby questioning the idea that biodiversity offsetting can completely reverse environmental damage.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. View raw image Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). View raw image Fig. 5. Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. 8 standard deviation (IS1 neg; IS2 neg), greater than 0. 8 standard deviation (IS1 pos; IS2 pos) and between ±0. 3 standard deviation (IS1 null; IS2 null). Numbers of events are displayed for each austral summer (S 85–86; S 86–87; S 87–88; S 88–89). Total number of pentads for each type of intraseasonal variability is indicated in parenthesis. View raw image Fig. 6. Anomaly correlation between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. [SEP] Justify:

Output: The text does not address the claim regarding microclimates and weather variations. It discusses technical details of mean outgoing longwave radiation and intraseasonal variability without mentioning microclimates or their impact on 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: Expression of PDE4A5catnull did not alter PDE4 activity in the hippocampus, prefrontal cortex or cerebellum (Figure 5—figure supplement 1A–C). Next, we sleep deprived mice for 5 hr and assessed whether the phosphorylation of LIMK and cofilin was altered in the hippocampus. In agreement with our hypothesis, we observed that 5 hr of sleep deprivation reduced both LIMK and cofilin phosphorylation in hippocampal lysates from eGFP mice (Figure 5I,J). PDE4A5catnull expression prevented the sleep deprivation-induced decreases in LIMK and cofilin phosphorylation (Figure 5I,J). While expression of PDE4A5catnull fully restored the pcofilin/cofilin ratio in the hippocampus of sleep deprived mice to the levels observed under non-sleep deprivation conditions, it should be noted that phosphatases such as slingshot (Sarmiere and Bamburg, 2004) may also contribute to the reduction in cofilin phosphorylation levels under conditions of sleep deprivation. Three hours of recovery sleep was sufficient to restore both LIMK and cofilin phosphorylation levels in the hippocampus (Figure 5K,L). The latter observation is in line with our previous observations that a few hours of recovery sleep is sufficient to restore hippocampal synaptic plasticity (Vecsey et al. , 2009). Figure 5 with 1 supplement see all Download asset Open asset Expression of catalytically inactive PDE4A5 in hippocampal neurons prevents memory deficits and alterations in the cAMP-PKA-LIMK-cofilin signaling pathway associated with sleep deprivation. (A) Mice were injected with pAAV9-CaMKIIα0. 4-eGFP or pAAV9-CaMKIIα0. 4-PDE4A5catnull-VSV into the hippocampus to drive neuronal expression of eGFP or catalytically inactive full-length PDE4A5 (PDE4A5catnull). (B) Robust PDE4A5catnull expression was observed at the expected molecular weight, 108 kDa, in hippocampal lysates. (C–E) PDE4A5catnullexpression was observed in all 3 subregions of the hippocampus. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that sleep deprivation in mice leads to a reduction in LIMK and cofilin phosphorylation in the hippocampus, indicating potential impairments in signaling pathways that are important for cognitive functions. Additionally, it notes that recovery sleep restores these phosphorylation levels, emphasizing the impact of sleep deprivation on cognitive processes such as memory.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: © Fauna & 2015 is rapidly expanding as a promising policy for allowing development and economic growth while achieving a Loss of biodiversity. This expansion is international. One of its key moments was the establishment of the Business and Programme (BBOP) in 2004 by a partnership of companies, financial institutions, government agencies, business and non-governmental organizations (BBOP, 2015). By 2011, at least 72 countries had either passed or were developing laws or policies related to biodiversity offsets or Loss (Madsen et al. , Reference Madsen, Carroll, Kandy and Bennett2011), and the EU has held a public consultation on a Loss policy (European Commission, 2015). In 2014 BBOP co-organized a conference in London with the inspired title of Biodiversity and Beyond. This was pitched as ‘the first global conference on approaches to avoid, minimize, restore, and offset biodiversity loss’, and brought together various corporations, governments and non-governmental organizations (BBOP, 2014). Biodiversity offsets are defined as ‘measurable conservation outcomes designed to compensate for significant residual adverse biodiversity impacts arising from project development after appropriate prevention and mitigation measures have been taken’ (BBOP, 2012a, p. 13). In effect, offsetting seeks to compensate for losses to biodiversity in one place (and at one time) by creating equivalent gains elsewhere. Thus forest cleared to make way for a development project might be compensated through the restoration of forest (or prevention of forest loss) somewhere else and in some cases at a later time. The appeal of offsetting to conservationists is its potential, when taken as part of the so-called mitigation hierarchy (BBOP, 2009), to deliver a Loss (or net gain) conservation outcome (BBOP, 2012a) by keeping a balance between nature destroyed by development and nature for conservation. The attraction of offsetting to developers is that it provides a practical, cost-effective and predictable process to address the environmental impacts of development while enabling the relocation of environmental compensation across space and time. The conservation science literature has so far focused on the technical challenges of implementing offsetting. There has been discussion of ways to equate ecological losses and gains in development and offset sites, to select appropriate biodiversity currencies, accounting systems and exchange rules, to address practical challenges for adequate monitoring, compliance and post-implementation evaluation, and to deal with perverse incentives (e. g. Bull et al. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that biodiversity offsets are designed to compensate for significant residual adverse biodiversity impacts rather than completely reversing all environmental damage. The text states that offsetting seeks to 'compensate for losses to biodiversity in one place by creating equivalent gains elsewhere,' which implies that it does not fully restore the original conditions but rather attempts to balance losses and gains.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. View raw image Fig. 7. Root-mean-square (rms) errors between forecasts of Z500 and reanalysis averaged over the SACZ (top) and region (40°S–20°N; 90°–20°W) (bottom). Plots on the left are for IS1 type, while plots on the right are for IS2 type. Dashed, dotted, and solid lines indicate positive, negative, and null cases, respectively. Horizontal axis indicates lead time in days. 30 Views 0 0 0 Views 235 105 4 PDF Downloads 104 28 4 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. A. Torbenson, and Quantifying the Role of Variability and from to at in India Authors: Divya Upadhyay, Sudhanshu Dixit, and Rainfall and Flooding from in the Mid-of the Authors: Yibing Su, James A. Smith, and Previous 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2 Page(s): 486–494 3060. Email: cjones@icess. ucsb. edu author address: Dr. Charles Jones, Institute for (ICESS), University of 93106-3060. Email: cjones@icess. ucsb. edu 1. IntroductionThe climatology of the large-scale circulation over shows intense convective activity during the austral summer, especially over the Amazon basin. Many previous studies have demonstrated that associated with the region of deep cumulus convection and intense precipitation, there is an upper-level anticyclone, the Bolivian high, and a downstream trough over northeast Brazil (Horel et al. 1989; Jones 1990; Lenters and Cook 1999). [SEP] Justify:

Output: The text is irrelevant to the claim as it provides technical information regarding weather forecasting errors and climatology of large-scale circulation without discussing the concept of microclimates or their impact on 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: (F–H) PDE4A5catnullwas not expressed in astrocytes reflected by a lack of co-labeling between PDE4A5catnull and GFAP expression. (I) Sleep deprivation causes a reduction in LIMK serine 596 phosphorylation in the hippocampus that is prevented by PDE4A5catnull expression (n = 7–8; two-way ANOVA, effect of virus F1,27 = 3. 299, p=0. 08; effect of sleep deprivation F1,27 = 6. 124, p=0. 02; interaction effect F1,27 = 11. 336, p=0. 002; eGFP SD group versus other groups p<0. 05). (J) Sleep deprivation causes a reduction in cofilin phosphorylation in the hippocampus that is prevented by PDE4A5catnull expression (n = 9–10; two-way ANOVA, effect of virus F1,35 = 4. 122, p=0. 05; effect of sleep deprivation F1,35 = 2. 885, p=0. 1; interaction effect F1,35 = 9. 416, p=0. 004; eGFP SD group versus other groups p<0. 05). (K, L) Three hours of recovery sleep after five hours of sleep deprivation restores hippocampal LIMK phosphorylation at serine 596 and cofilin phosphorylation at serine 3 to those observed in non-sleep deprived controls (p>0. 45 for both comparisons). (M) Mice expressing eGFP or PDE4A5catnull were trained in the hippocampus-dependent object-place recognition task and immediately sleep deprived for 5 hr after training (SD) or left undisturbed (NSD). Hippocampal PDE4A5catnull expression prevents memory deficits caused by sleep deprivation (n = 8–10; two-way ANOVA, effect of virus F1,33 = 2. 626, p=0. 115; effect of sleep deprivation F1,33 = 2. 311, p=0. 138; interaction effect F1,33 = 7. 485, p=0. 01; posthoc Dunnet test eGFP SD group versus other groups p<0. 05). In all blots, each lane represents one individual animal. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that sleep deprivation leads to memory deficits, as indicated by the phrase "hippocampal PDE4A5catnull expression prevents memory deficits caused by sleep deprivation." This suggests that cognitive functions that depend on the hippocampus, such as learning and memory, are impaired by sleep deprivation.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: , Reference Bull, Suttle, Gordon, Singh and Milner-Gulland2013; Gardner et al. , Reference Gardner, von Hase, Brownlie, Ekstrom, Pilgrim and Savy2013; Pilgrim et al. , Reference Pilgrim, Brownlie, Ekstrom, Gardner, von Hase and ten Kate2013; Gordon et al. , Reference Gordon, Bull, Wilcox and Maron2015). Despite criticisms (e. g. Quétier & Lavorel, Reference Quétier and Lavorel2011; Maron et al. , and Gardner2012) this literature tends to approach offsetting as a neutral conservation tool and take for granted the role of offsetting as a solution to the problem of the environmental impacts of development or even as a means to achieve sustainable development (e. g. Bayon et al. , Reference Bayon, Fox and Carroll2008; Gordon et al. , Reference Gordon, Bull, Wilcox and Maron2015). The implications of offsetting are more substantial than these essentially technical discussions suggest. The underlying promise of offsetting, namely the production of equivalent natures (E. Apostolopoulou & W. M. Adams, unpubl. data), has the potential to bring about a profound change both to the conception of nature and the practice of conservation. Here, we consider these changes and their implications, using the concept of policy frames (Entman, Reference Entman1993). We define framing as the act of defining problems, diagnosing their causes, making moral judgements and suggesting remedies (Tuchman, Reference Tuchman1978; Apostolopoulou & Paloniemi, and Paloniemi2012). Focusing on policy frames can shed light on the implicit politics of presenting (and chiefly analysing) offsetting as neutral and free of ideology. The latter is important since framing is a critical and unavoidable element of policy-making and thus an understanding of the way a policy issue is framed is essential (Lakoff, Reference Lakoff2010) if the consequent policy options and underlying value struggles (Sullivan & Hannis, and Hannis2015) are to be unravelled and properly assessed. The way nature is discussed in debates about development (as when it is set within a frame of thinking about offsetting) affects the chances that it will survive or be destroyed. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of biodiversity offsetting as a conservation tool. It highlights that despite being viewed as a solution to environmental impacts and sustainable development, there are criticisms of its effectiveness. The text suggests that the implications of offsetting are more complex and substantial than its portrayal as neutral. By indicating that framing biodiversity offsetting can have significant political and ideological implications, it questions the adequacy of offsetting in truly reversing environmental damage, thus challenging the notion that it can completely address the negative impacts of development.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: In contrast, the weakening of the SACZ is related to suppressed convection in the central Pacific but enhanced convective activity in the western and eastern Pacific. The different behavior in the predictability during strong versus weak SACZ events deserves additional investigation. The purpose of this note is to further motivate research on the influence of intraseasonal variations in the predictive skill over the SACZ. As is pointed out above, the SACZ exhibits a wide range of intraseasonal variability. Therefore, this study considers the errors in numerical weather forecasts in for two types of variations: 30–70 days and 10–30 days. The former is directly related to the MJO, while the latter is still intraseasonal but not necessarily linked to the 30–60-day oscillation. 2. MethodologyIn this study we use results from the recent (DERF) experiment concluded with the reanalysis version of the Forecast (MRF) model (Schemm et al. 1996; see also Jones et al. 1998). The present DERF experiment includes 50-day forecasts, initialized once a day (0000 UTC) with reanalyses fields, and covering the 5-yr period from 1 January 1985 to 31 December 1989. In the present version of the DERF forecast runs, the sea surface temperature (SST) is damped from its initial observed values to climatology using a 90-day e-folding time. The reanalysis version of the MRF model is identical to the NCEP global operational model of 1996, except the horizontal spectral resolution is reduced to T62 (equivalent to 210 km) (Kalnay et al. 1996). The NCEP–NCAR reanalysis fields at 0000 UTC are used for forecast verification and we limit our discussion to errors in 500-hPa geopotential height (Z500) in the austral summer (1 November–31 March; 1985–86, 1986–87, 1987–88, and 1988–89). Time filtering was applied to verification and forecast fields in order to compute anomalies from the annual cycle, which was determined by averaging the 5 yr of reanalysis and computing the first two harmonics. The annual cycle constructed with the mean and first two harmonics was then subtracted from the time series of Z500. No other filtering is applied to the forecasts and reanalysis. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses the influence of intraseasonal variations in predictive skill over the South American Convergence Zone (SACZ) and the methodology used in a specific weather forecasting experiment. It does not address the concept of microclimates or their impact on 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: NSD: non-sleep deprived, SD: sleep deprived, SD+RS: sleep deprived plus recovery sleep. Scale bar, 100 µm. Values represent the mean ± SEM. *p<0. 05 by posthoc Dunnet’s posthoc test. See also Figure 5—figure supplement 1. Figure 5—source data 1 Recovery sleep following sleep deprivation restores LIMK and cofilin phosphorylation levels in the hippocampus, and expression of an inactive version of PDE4A5 in hippocampal neurons prevents memory deficits associated with sleep deprivation. (K) The data source file contains the relative optical density values (in arbitrary units) of the pLIMK and LIMK western blots for each individual animal of both the non-sleep deprived control group (NSD) and the group that underwent 5 hr of sleep deprivation followed by 3 hr of recovery sleep (SD + RS). (L) The data source file contains the relative optical density values (in arbitrary units) of the pcofilin and cofilin western blots for each individual animal of both the non-sleep deprived control group (NSD) and the group that underwent 5 hr of sleep deprivation followed by 3 hr of recovery sleep (SD + RS). (M) The data source file contains the object exploration times for the displaced (DO) and non-displaced objects (NDO1, NDO2) for each individual animal of each group. Download elife-13424-fig5-data1-v3. xlsx Blocking PDE4A5 function in hippocampal neurons prevents memory deficits caused by sleep deprivation Because PDE4A5catnull expression in hippocampal neurons prevents changes in the cAMP-PKA-LIMK-cofilin pathway caused by sleep deprivation, we hypothesized that expression of PDE4A5catnull in hippocampal excitatory neurons would also prevent the memory deficits induced by 5 hr of sleep deprivation. Mice expressing eGFP showed a clear preference for the displaced object 24 hr after training, which was lost in animals that were deprived of sleep for 5 hr immediately after training (Figure 5M). In contrast, mice expressing PDE4A5catnull showed a strong preference for the displaced object despite sleep deprivation (Figure 5M). [SEP] Justify:

Output: The text corroborates the claim by providing evidence that sleep deprivation impairs cognitive functions, specifically memory. It states that mice deprived of sleep for 5 hours immediately after training lost their ability to show preference for a displaced object in comparison to those that had recovery sleep. This supports the idea that sleep deprivation adversely affects cognitive functions such as learning and memory.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: (Reference Monbiot2014) observes, offsetting reframes the issue of conservation: ‘those who believe they can protect nature by adopting this frame are stepping into a trap their opponents have set’. Here, we identify four ways in which offsetting achieves such a reframing of biodiversity and its conservation and we explore the ways it changes the options for conservation by bringing in and disallowing certain kinds of arguments. Reframing biodiversity conservation Offsetting reframes non-human nature as a score of isolated biodiversity units In offsetting, biodiversity and ecosystems are defined, valued and characterized in terms of quantitative units or credits that are used to represent selected ecosystem attributes and are considered exchangeable across space and time (e. g. DEC, 2006; Defra, 2013). The selection of units of measurement, currencies and rules for exchanges between different sites has proven inherently challenging (Tucker et al. , and Rayment2013). The attempt to design simple and practical offset metrics has often made them highly reductionist (e. g. the simple habitat area ratio based metrics widely used in Germany and the USA, and more sophisticated metrics such as the Hectares offsetting scheme initially developed in Victoria; Tucker et al. , and Rayment2013; IEEP, 2014). In the UK concerns have been raised that the offsetting process will resemble a fast ‘box-ticking exercise’ that is inadequate to assess a site's year-round biodiversity (Kinver, Reference Kinver2013). The creation of offset metrics to represent ecological losses and gains through numerical scores (Environment Bank, 2013a) involves a narrowing of focus to isolated parts of an ecosystem. This narrowing is fundamental to offsetting calculations and reproduces the reductionist myth of simplicity (Levins & Lewontin, and Lewontin1980). This is inherently unsatisfactory and impoverishes both the advance of theory and conservation practice because it traps ecology in reductionist strategies involving a continuous retreat from the study of intrinsically complex systems (Levins & Lewontin, and Lewontin1980). Ecosystems are dialectically composed, dynamic, multi-layered systems that do not form simple mappable units (Boitani et al. , Reference Boitani, Mace and Rondinini2015), and biodiversity is non-interchangeable in terms of type, space and time (Walker et al. , Reference Walker, Brower, Stephens and Lee2009). [SEP] Justify:

Output: The text contrasts the claim by arguing that biodiversity offsetting does not effectively reverse environmental damage. It emphasizes that offsetting reframes biodiversity in a reductionist manner, suggesting that the process simplifies complex ecosystems into quantitative units, potentially leading to inadequate assessments of biodiversity. In particular, phrases like 'this is inherently unsatisfactory and impoverishes both the advance of theory and conservation practice' indicate that offsetting may mislead conservation efforts rather than completely reversing environmental damage.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Intraseasonal convective activity over the SACZ is selected according to the magnitude of OLR anomalies. Pentads of OLR were obtained from NCEP from 1–5 January 1979 through 27–31 December 1996. To obtain intraseasonal anomalies, two Lanczos band-pass filters with 49 weights were applied to the OLR pentads [see Jones et al. (2000) for details on time filtering]. Figure 1 shows the frequency responses of the two band-pass filters: 30–70 days and 10–30 days. The first band is denoted here as intraseasonal type 1 (IS1), while the second is intraseasonal type 2 (IS2). 3. ResultsBefore examining the skill of the MRF model over the SACZ region for each band of intraseasonal variations, it is instructive to discuss the mean summertime Z500 and the standard deviation fields (Fig. 2). The mean Z500 (top) indicates a midtroposphere maximum associated with the Bolivian high and strong horizontal gradients in subtropical and middle latitudes of South America. The total standard deviation in Z500 (bottom) shows, as expected, small magnitudes near the equatorial region and increasing variability toward the midlatitudes of the Southern Hemisphere. Anomaly correlations between Z500 forecasts and verification fields for all days during the four summer seasons were computed for 1–20 days lead time (Fig. 3). The MRF model shows higher skill (correlations greater than 0. 7 for 1 day lead time) in a narrow band over subtropical South America. Note that near the equatorial region the skill is less and progressively breaks down toward higher latitudes as the lead time increases. These results can be compared to the errors in the forecasts for each IS1 and IS2 band discussed next. Figure 4a displays the mean OLR during the austral summer and Fig. 4b shows the total standard deviation (i. e. , variations greater than two pentads and shorter than the seasonal average). Although the lowest values of OLR, indicative of strong tropical convection, are observed over the Amazon basin, the largest variability is found over eastern South America. The standard deviations of OLR anomalies (OLRA) in the 30–70 days and 10–30 days are shown in Figs. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses intraseasonal convective activity, OLR anomalies, model skill over a specific region, and associated meteorological phenomena without addressing the concept of microclimates or their effects on 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: Brief sleep deprivation after training in the object-place recognition task resulted in a loss of preference for the displaced object in mice expressing PDE4A5catnullΔ4 (Figure 5—figure supplement 1I). The inability of PDE4A5catnullΔ4 to prevent the memory deficit caused by sleep deprivation was not a consequence of altered exploration levels during training (Figure 5—figure supplement 1H). This finding indicates that the memory rescue in the previous experiment was a result of the full length PDE4A5catnull being sequestered to specific signalosomes through the isoform-unique N-terminal region rather than a consequence of PDE4A5catnull being unable totarget the functionally relevant complexes sequestering full length PDE4A5. It also indicates that displacing sequestered, active endogenous PDE4A5 in hippocampal excitatory neurons is sufficient to prevent memory deficits induced by 5 hr of sleep deprivation. Overall, these data suggest that sleep deprivation negatively impacts spine numbers by targeting the PKA-LIMK-cofilin pathway through the alterations in activity of PDE4A5 (Figure 6). Figure 6 Download asset Open asset The impact of sleep deprivation on hippocampal spine dynamics. Sleep deprivation increases PDE4A5 protein levels that cause a reduction in cAMP levels and attenuation of the PKA-LIMK signaling pathway, which results in a reduction in the phosphorylation of cofilin. Dephosphorylated cofilin can lead to spine loss. Suppressing PDE4A5 function through viral expression of a catalytically inactive PDE4A5 prevents alterations in LIMK and cofilin signaling as well as the cognitive impairments caused by sleep deprivation. Likewise, attenuating cofilin activity through viral expression of a catalytically inactive form of cofilin prevents the loss of dendritic spines, impairments in synaptic plasticity, and memory deficits associated with sleep loss. Proteins whose function is reduced after sleep deprivation are shown in blue. Proteins whose function is promoted by sleep deprivation are shown in red. of the major challenges in sleep research is the elucidation of molecular mechanisms and cellular circuits underlying the adverse consequences of sleep loss. Here, we use in vivo rescue experiments to define a critical molecular mechanism by which brief sleep deprivation leads to cognitive impairments. [SEP] Justify:

Output: The text corroborates the claim by providing detailed evidence that sleep deprivation negatively impacts cognitive functions, particularly memory, as indicated by the results of the object-place recognition task in mice. It states that 'brief sleep deprivation... resulted in a loss of preference for the displaced object,' which suggests impairment in memory and cognitive processing. Furthermore, it discusses how sleep deprivation leads to alterations in various signaling pathways that ultimately cause cognitive impairments, such as memory deficits. This directly supports the claim that sleep deprivation impairs cognitive functions.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: No single surrogate (or even a series of them) can entirely capture biodiversity, since not all biodiversity attributes are measurable, and therefore it is impossible to guarantee that no biodiversity is lost (and thus that Loss is actually achieved; BBOP, 2012b; Bull et al. , Reference Bull, Suttle, Gordon, Singh and Milner-Gulland2013; Gardner et al. , Reference Gardner, von Hase, Brownlie, Ekstrom, Pilgrim and Savy2013). The use of reductionist metrics is common in conservation but the way they are used in offsetting is distinctive in several critical ways. Firstly, offsetting metrics incorporate assumptions about future states of nature, both in terms of future rates of loss (which conservation action can be predicted to slow; Seagle, Reference Seagle2012) and in terms of the potential of ecological restoration. In reframing biodiversity as fully replaceable and re-creatable by human action, offsetting deliberately confuses the state of ecological restoration science and practice with its aspiration. Restoration ecology has advanced greatly in sophistication but it is ‘not a magic bullet that provides instant ecosystems of the desired type’ (Menz et al. , Reference Menz, Dixon and Hobbs2013). Techniques such as the relocation of soils from on the route of the HS2 high-speed train in the UK are at best experimental (HS2, 2013) and many studies have proven that the majority of offsets do not deliver what they promise (e. g. Kettlewell et al. , and Fay2008). Secondly, as a standard procedure, offsetting conflates the state of nature with other factors, for example applying crude multipliers to address issues such as time lags between biodiversity loss and future gain in an offset site (Eftec, IEEP et al. , 2010; HS2, 2013), or the distance between the development and offset sites (IEEP, 2014). Such calculations raise fundamental problems of incommensurability because such issues cannot possibly be adequately addressed by simply increasing the number of credits required to offset the damage caused in a development site. Thirdly, the crude reductionism of offsetting metrics is not a technical issue that more scientific data can resolve. On the contrary, it is a defining characteristic of the offsetting logic itself. [SEP] Justify:

Output: The text contrasts the claim by explaining that biodiversity offsetting cannot fully capture or restore biodiversity, as not all attributes of biodiversity are measurable. It highlights that it is impossible to guarantee that no biodiversity is lost, which undermines the notion that offsetting can completely reverse environmental damage. Furthermore, it mentions that restoration ecology is not a magic bullet and that most offsets do not deliver their promised outcomes, indicating the inadequacy of offsetting in fully reversing ecological harm.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: 4c,d respectively, and indicate that intraseasonal variations account for a large part of the total variance (Liebmann et al. 1999). Based on the spatial pattern of the OLRA variability, we defined a base region (solid lines) in which the magnitudes of the OLRA were used to characterize periods of weak (strong) SACZ. The extent of the base region is chosen to represent the mean position of the SACZ. A time series of 30–70-day OLRA spatially averaged in the base region was constructed and periods above (below) 0. 8 (−0. 8) standard deviation during the austral summer were selected. The weak events are denoted IS1 positive (IS1 pos), while the strong are IS1 negative (IS1 neg). In addition, periods of very inactive SACZ were selected when the magnitudes of the 30–70-day OLRA values in the base region were between ±0. 3 standard deviation (IS1 null). Likewise, a time series of 10–30-day OLRA was constructed over the base region. Periods of weak IS2 variability were selected when OLRA are above 0. 8 standard deviation (IS2 pos); strong when OLRA are below −0. 8 standard deviation (IS2 neg); and null events when OLRA are between ±0. 3 standard deviation (IS2 null). The frequency distributions of the selected IS1 and IS2 events per season are plotted in Fig. 5. For the IS1 type (top) there are a total of 27 (IS1 pos), 29 (IS1 neg), and 32 (IS1 null) pentads, which represent 11, 11, and 20 independent events, respectively. Similarly, for IS2 type there are 23 (IS2 pos), 23 (IS2 neg), and 34 (IS2 null) pentads, so that 20, 19, and 22 are independent events. We consider independent events nonconsecutive pentads. The skill of the MRF model in forecasting Z500 was examined for each sample of intraseasonal variability defined above (IS1 and IS2). Two skill measures, anomaly correlation and root-mean-square (rms) error, were computed for samples IS1 pos, IS1 neg, IS1 null, IS2 pos, IS2 neg, and IS2 null. [SEP] Justify:

Output: The text does not address the concept of microclimates or their ability to create weather variations over short distances. Instead, it discusses intraseasonal variability and the parameters associated with the SACZ (South American Monsoon System), which are irrelevant to the claim. Therefore, it is classified as 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: First, we show that sleep deprivation dramatically reduces spine number and dendrite length of hippocampal CA1 neurons without affecting dendritic structure of CA3 neurons. Second, we demonstrate that sleep deprivation increases cofilin activity in the hippocampus, but not the prefrontal cortex, which is a likely explanation for the reductions in CA1 spine numbers and dendrite length. Third, we find that three hours of recovery sleep restores spine number, dendrite length and cofilin phosphorylation levels to those observed in non-sleep deprived mice. Fourth, we show that suppression of cofilin activity in hippocampal excitatory neurons is sufficient to prevent sleep-deprivation-induced decreases in dendritic spines number, LTP impairments, and memory. Fifth, we demonstrate that hippocampal expression of constitutively active cofilin is sufficient to cause long-term memory deficits but not LTP impairments. Sixth, we find that suppression of PDE4A5 function through overexpression of a catalytically inactive mutant version of PDE4A5 changes LIMK and cofilin phosphorylation levels caused by sleep deprivation. Finally, we show that hippocampal suppression of PDE4A5 function prevents the negative impact of sleep deprivation on memory consolidation. Thus, our studies demonstrate that changes in the cAMP/PKA/LIMK/cofilin pathway are necessary to cause memory deficits under conditions of sleep deprivation. In light of the fact that elevated cofilin activity can lead to spine shrinkage and spine loss (Zhou et al. , 2004; Pontrello et al. , 2012), our genetic manipulations of cofilin and PDE4A5 signaling independently link impairments in synaptic plasticity and memory caused by brief sleep deprivation with the loss of dendritic spines in the hippocampus. Deficits in synaptic plasticity and memory both represent read outs of the impact of sleep deprivation on hippocampal function, but our work does not directly examine the direct relationship between synaptic plasticity and memory, a topic that has been the subject of extensive study and discussion in the literature (Lynch, 2004; Sah et al. , 2008). [SEP] Justify:

Output: The text corroborates the claim by indicating that sleep deprivation leads to changes in hippocampal neurons, specifically the reduction of spine number and dendrite length, which are associated with memory deficits and impairments in synaptic plasticity. The findings highlight the relationship between sleep deprivation and its negative effects on memory consolidation and cognitive functions. This supports the assertion that sleep deprivation impairs cognitive functions, including learning ability.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: The promise of offsetting to resolve the longstanding conflicts between economic development and conservation (Bull & Brownlie, and Brownlie2016) reproduces the classic ‘win–win’ scenario of sustainable development and depends significantly on the reductionism of its metrics. Offsetting is useful precisely because the efficiency and cost-effectiveness of its methodologies make the quantification of losses and gains in planning more straightforward, less costly and less time consuming than alternative approaches (e. g. Defra, 2013). The purpose of offsetting is not conservation as such, but to ‘give greater certainty for businesses’ in their development planning (Commonwealth of Australia, 2012, p. 4). Current international best practice in offsetting (Gordon et al. , Reference Gordon, Bull, Wilcox and Maron2015) is represented by the Business and Programme launched by Forest Trends, an organization created to promote market-based approaches to conservation and bridge ‘traditional divides’ between industry, donors and environmental groups (Forest Trends, 2015). Ecosystems are sliced into biodiversity units precisely to simplify the measurement of development impacts, and these units are purposefully separated from their ecological context through functional and spatial abstraction (Robertson, Reference Robertson2004; Sullivan, Reference Sullivan2013a; see also Bumpus & Liverman, and Liverman2008, about carbon offsets) to become equivalent and thus to allow their exchange across space and time. Interestingly, Robertson (Reference Robertson2006, p. 384) notes that the ‘rapid assessment methods’ that are currently used in wetland mitigation banking in the USA are the descendants of much more complex rapid assessment methods developed in the early 1980s, when wetland banking was performed only non-commercially. Offsetting therefore champions mechanistic reductionism to quantify biodiversity in a way that makes it possible to speak about it ‘in business terms’ (Baker, Reference Baker2014) and make non-human nature something ‘that capital can see’ (Robertson, Reference Robertson2006). Offsetting removes the place specificity of nature In its representation of non-human nature as biodiversity units, offsetting explicitly reframes the links between biodiversity, conservation and place at the most fundamental level by reworking nature's specific relation to place. Location is critical to biodiversity both in biogeographical terms (biodiversity reflects the geophysical context; Comer et al. [SEP] Justify:

Output: The text contrasts the claim by explaining that biodiversity offsetting does not aim to conserve biodiversity but rather to facilitate economic development by making planning more efficient, which indicates that it cannot completely reverse environmental damage. It highlights that offsetting simplifies the measurement of development impacts and distances itself from the ecological context of biodiversity, suggesting that the approach is fundamentally limited in its ability to restore all ecological harms.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: The statistics were then averaged over the SACZ base region indicated in Fig. 4 (solid lines) and over the region defined by (40°S–20°N; 90°–20°W). Figure 6 shows the anomaly correlation averaged over the SACZ domain (top) and over the domain (bottom). The anomaly correlation during IS1 averaged over the SACZ domain (top left) indicates that the correlations start at about the same values (0. 65) for IS1 pos, IS1 neg, and IS1 null for 1-day lead time. Note that these values are averaged over the SACZ domain and the MRF model shows higher skill farther south (see Fig. 3). However, as forecast time progresses, the anomaly correlations for IS1 null are systematically smaller than IS1 pos and IS1 neg. Correlations of 0. 3 persist up to 10 days lead time for IS1 pos and IS1 neg. The anomaly correlations during IS2 averaged over the SACZ (top right) show similar magnitude (0. 65) for 1 day lead time. In contrast, as lead time increases, the anomaly correlations for IS2 pos and IS2 neg are smaller than for IS2 null. In particular, during periods of strong convective activity on 10–30 days (IS2 neg) forecasts are less skillful. The different behavior of the MRF forecasts during IS1 and IS2 periods are still noticeable in the averages over the domain (bottom right and left), although the differences in the magnitudes of the anomaly correlations among positive, negative, and null cases are significantly smaller. However, the differences in the magnitudes of the correlation coefficients do not pass the 95% significance level. The rms errors for the IS1 type averaged over the SACZ region (Fig. 7; top left) show that the MRF model has more skill in predicting Z500 during periods of strong convective activity (IS1 neg) as opposed to periods of suppressed (IS1 pos) or weak (IS1 null) activity. In addition, the growth rate of rms errors during IS1 pos is larger than in other periods. These results are consistent with the results of NPMP. In contrast, the rms errors for the IS2 type seem to exhibit a different behavior (Fig. 7; top right). [SEP] Justify:

Output: The text does not address the concept of microclimates or their effects on weather variations. It primarily discusses statistical correlations and model forecasts over specific geographic regions and timeframes, without linking to the claim about microclimates creating weather variations. Therefore, it is considered 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: That care should be taken to directly relate LTP deficits with memory impairments is emphasized by our findings that expression of constitutively active cofilin is sufficient to cause memory deficits while it does not impact at least one form of L-LTP that is disrupted by sleep deprivation (Vecsey et al. , 2009). We observed a significant reduction in the total number of dendritic spines of excitatory CA1 neurons after 5 hr of sleep deprivation. This substantial decrease in dendritic spine number occurs rapidly and exceeds the fluctuations in hippocampal spine number observed across the estrus cycle (González-Burgos et al. , 2005), or the changes in spine number caused by stress (McLaughlin et al. , 2005; Shors et al. , 2001). In contrast to sleep deprivation, acute stress results in an increase rather than a decrease in CA1 spine number in male rats (Shors et al. , 2001). Even 3 to 4 weeks of chronic stress or systemic delivery of corticosterone does not alter dendritic arborization of CA1 neurons (McLaughlin et al. , 2005). It is also unlikely that other factors associated with the procedure to keep animals awake rather than sleep deprivation per se causes the spine loss as our previous work indicated that applying the exact same amount of stimulation in the waking phase (i. e. the dark phase) does not lead to memory impairments (Hagewoud et al. , 2010a). In line with our finding of reductions in spines during sleep deprivation, work by Yang and colleagues revealed that sleep promotes dendritic spine formation in neurons activated by learning (Yang et al. , 2014). Combined with our work, these experiments suggest that sleep deprivation disrupts learning-induced changes in spines that occur during sleep. Importantly, our structural studies reveal that spine loss is reversed by recovery sleep, consistent with this idea. Thus, our work reveals a distinct, selective, and rapid effect of brief periods of sleep loss on synaptic structure. It is noteworthy that even a short period of sleep deprivation acts to trigger such a dramatic effect on neuronal structure, which is reversed by recovery sleep. Studies assessing the impact of sleep deprivation on electrophysiological properties of excitatory hippocampal neurons suggest that sleep deprivation negatively impacts long-lasting forms of LTP (Havekes et al. , 2012a; Abel et al. , 2013). [SEP] Justify:

Output: The text corroborates the claim by detailing how sleep deprivation affects neuronal structures such as dendritic spines and long-term potentiation (LTP), which are crucial for learning and memory. The findings indicate that sleep deprivation quickly causes significant reductions in dendritic spines and suggests that it disrupts learning-induced synaptic changes. Additionally, it highlights that recovery sleep can reverse the impairments caused by sleep deprivation, linking these changes directly to cognitive functions impacted by lack of sleep.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: , and Heller2015) and in cultural terms (biodiversity in situ reflects historical human management or impact, and is reflected in extant cultural values). Estimations of conservation value have long recognized the importance of history, culture and place in conservation sites (e. g. criteria of typicality, recorded history, position in an ecological or geographical unit, and intrinsic appeal; Ratcliffe, Reference Ratcliffe1977). In offsetting, the biological and social characteristics of places are treated only as representative of a standard category that can be replicated in the offset site (for example the Australian government argues that offsets can in some circumstances also compensate for adverse impacts on heritage values; Commonwealth of Australia, 2012). Thus although the need to consider the cultural or social values of biodiversity is often recognized by the advocates of offsetting (e. g. DEC, 2006; BBOP, 2012a), such factors are typically absent in technical debates. The abstracted biodiversity units used in offset metrics (e. g. Tucker et al. , and Rayment2013) take no account of the cultural or historical importance of place and the social ties between communities and particular habitats and ecosystems. This is not, however, just a technical limitation of the metrics in use but rather a consequence of the way the core logic of offsetting reframes nature's place. By denying social history to landscapes, offsetting promotes a techno-managerial vision for conservation (Adams, Reference Adams2015; c. f. Asafu-Adjaye et al. , Reference Asafu-Adjaye, Blomqvist, Brand, Brook, de Fries and Ellis2015) and frames the latter within a ‘flat world’ (Friedman, Reference Friedman2005), where exchanges of ecological losses and gains can be separated from their ecological, cultural, socio-economic and political context. Offsetting often involves a notional trade with offset sites far from the development sites (e. g. Robertson, Reference Robertson2000; see also the EU discussion on offset trades across national borders; European Commission, 2013). In the process, cultural engagements with place are disrupted or lost, and public access to conservation sites, biodiversity and more generally to green spaces may be changed (lost in one place, gained somewhere else) or restricted (e. g. if an accessible habitat is replaced with one under strict protection; Seagle, Reference Seagle2012). [SEP] Justify:

Output: The text contrasts the claim by highlighting the limitations of biodiversity offsetting. It explains that offsetting often overlooks cultural, historical, and social ties to various habitats and ecosystems, leading to a techno-managerial approach that fails to account for the contextual significance of biodiversity. The mention of ecological losses and gains being separated from their socio-economic and political context suggests that biodiversity offsetting does not completely reverse environmental damage, but rather may disrupt local cultural engagements with biodiversity.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: The MRF model has less skill in forecasting Z500 during periods of strong (IS2 neg) and suppressed (IS2 pos) convection than in periods of weak (IS2 null) activity. In particular, the growth rate of rms error during IS2 pos is larger than for IS1 neg. A somewhat similar behavior is observed when the rms errors for IS1 and IS2 are averaged over the domain (Fig. 7; bottom left and right, respectively). 4. Concluding remarksThe summer season in exhibits strong convective activity over the Amazon basin and SACZ, with significant intraseasonal variability found in the eastern part of the continent. The recent study of NPMP showed that intraseasonal variations in the 10–90 days band impact the skill of numerical weather forecasts over the PAN region. In particular, their results pointed out that initial forecast errors during periods of weak convective activity over the SACZ are smaller than for strong events. As is discussed in previous studies, intraseasonal variability over the SACZ is modulated by the MJO as well as by shorter period variations (10–30 days). The preliminary results discussed here indicate that the MRF model has more skill (i. e. , in terms of rms errors) during periods of strong convective activity associated with the MJO as opposed to periods of suppressed or weak activity. In contrast, the MRF model has less skill during periods of strong or suppressed convection associated with 10–30 days variations. Additional investigation in this research area should be useful to understand how remotely forced intraseasonal variability over the SACZ can impact local forecasts. This is especially important since the climatological position of the SACZ coincides with the densest demographic region in South America. 3555. Horel, J. D. , A. N. Hahmann, and J. E. Geisler, 1989: An investigation of the annual cycle of convective activity over the tropical Americas. J. Climate,2, 1388–1403. Jones, C. , 1990: An investigation of low-frequency variability of the large-scale circulation over South America. M. S. thesis, Department of Meteorology, University of Utah, 108 pp. ——, and J. D. Horel, 1991: High circulation and convective activity over (in Portuguese). Rev. [SEP] Justify:

Output: The text discusses the performance of the MRF model in forecasting weather and the impact of convective activities on forecast accuracy. However, it does not directly address or provide evidence regarding the creation of microclimates or variations in weather over short distances. Thus, it is considered 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: In this study and our previous work (Vecsey et al. , 2009; Prince et al. , 2014), we showed that 5 hr of sleep deprivation attenuates long-lasting forms of LTP in the hippocampus. We observed that expression of an inactive mutant form of cofilin prevented the reductions in CA1 spine number, the impairment in a long-lasting form of LTP caused by sleep loss. It is interesting to note that three hours of recovery sleep not only restores spine numbers in CA1 neurons, but also hippocampal LIMK and cofilin phosphorylation levels. These findings complement our previous electrophysiological studies, in which we showed that such a short period of recovery sleep also restores deficits in LTP caused by 5 hr of sleep deprivation (Vecsey et al. , 2009). Our work reveals that PDE4A5 is a critical mediator of the impact of sleep deprivation on memory consolidation. Indeed, one reason why hippocampal area CA1 is specifically vulnerable to sleep deprivation may be the high level of PDE4A5 expression in this region (McPhee et al. , 2001). Specific PDE4 isoforms are sequestered by distinct signalosome complexes that regulate localized cAMP signaling and impart functionally distinct roles (Houslay, 2010). Impairing the function of PDE4A5 signalosomes through expression of a full length catalytically inactive form of PDE4A5 exerts a dominant negative action, phenotypically identified here as preventing the alterations in LIMK and cofilin signaling caused by sleep deprivation. This makes memory consolidation resistant to the negative impact of sleep loss. Consistent with the notion that a key functional role of the isoform-unique N-terminal region of PDE4 isoforms is the targeting to signalosomes so as to exert functionally distinct actions (Houslay, 2010), the hippocampal expression of a catalytically in active version of PDE4A5 lacking the isoform unique N-terminal domain fails to rescue the cognitive deficits associated with sleep loss. The latter observation suggests that the isoform-specific N-terminal domain of PDE4A5 targets this specific PDE isoform to signalosomes that degrade cAMP in the vicinity of complexes that are particularly sensitive to sleep deprivation such as the complexes that contain LIMK and cofilin. [SEP] Justify:

Output: The text corroborates the claim by discussing how sleep deprivation specifically affects memory consolidation and long-term potentiation (LTP), which are crucial for cognitive functions. It implies that due to the impairments in the signaling mechanisms (such as the activity of PDE4A5) triggered by sleep loss, cognitive deficits arise. By stating that 'the hippocampal expression of a catalytically inactive version of PDE4A5 fails to rescue the cognitive deficits associated with sleep loss,' the text supports the notion that sleep deprivation does indeed impair cognitive functions, particularly in memory-related tasks.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: Under the surface of an apparently technical process to calculate equivalence, offsetting in fact establishes a new policy frame that has the potential to create outcomes that are socially and spatially uneven (as there are always specific winners and losers to such exchanges of environmental goods and bads; e. g. Ruhl & Salzman, and Salzman2006; Sullivan, Reference Sullivan2013a). Even though offset metrics may calculate the importance of the places lost only in terms of ecological units, they do so by portraying nature as external to society and by ignoring any links between people and nature, the result is a total remaking of places (both in the development and the offset sites) in a way that reflects an increasing social reproduction of non-human nature driven by specific corporative interests and not by concerns over socio-environmental and spatial justice. Offsetting reframes conservation as an exchange of credits Biodiversity offsetting is a part of the fundamental shift in the way we think about non-human nature towards the economic valuation of ecosystem services and natural capital (Sukhdev et al. , Reference Sukhdev, Wittmer, Miller, Helm and Hepburn2014; c. f. on Natural Capital, 2015). In the language of offsetting, pre-existing conservation sites are reframed as territories providing ecosystem services (ten Kate et al. , Reference ten Kate, Bishop and Bayon2004) and ecological credits are framed as products owned by prospective ‘sellers’ who are in turn advised how to know better the ‘value’ of their product when selling it to prospective ‘buyers’ (Roberts & Waage, and Waage2007). Conservation activities are thus becoming part of confidential commercial transactions over land for the creation of offset sites (Agreements, Ecosystem Marketplace, 2015). While the translation of nature from the scientific language of ecosystems into the financial language of capital is generic, reflecting the wider shift towards neoliberal approaches to conservation (Büscher et al. , Reference Büscher, Sullivan, Neves, Igoe and Brockington2012; Apostolopoulou & Adams, and Adams2015), offsetting is quite specific in its dependence on a market-based frame for conservation. Offsetting reframes conservation action as an exchange of ecological credits, where numerical scores that are considered equivalent in both ecological and monetary terms represent nature lost, saved or recreated. [SEP] Justify:

Output: The text contrasts the claim by highlighting that biodiversity offsetting does not completely reverse environmental damage. Instead, it argues that offsetting creates uneven social and spatial outcomes, undermines the link between people and nature, and reframes conservation as an economic transaction rather than a genuine restoration of ecological integrity. Concepts such as 'specific winners and losers' along with the emphasis on 'numerical scores' instead of holistic environmental recovery directly challenge the idea that biodiversity offsetting can achieve complete reversibility of damage.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Brasileira de Meteorologia,5-1, 379–387. ——, D. E. Waliser, J. E. Schemm, and W. K. Lau, 2000: Prediction skill of the Madden and in dynamical extended range weather forecasts. Climate Dyn. , in press. Kalnay, E. , and Coauthors, 1996: NCEP/NCAR 40-Project. Bull. Amer. Meteor. Soc. ,77, 437–471. Lenters, J. D. , and K. H. Cook, 1999: Summertime precipitation variability over South America: Role of the large-scale circulation. Mon. Wea. Rev. ,127, 409–431. Liebmann, B. , G. N. Kiladis, J. A. Marengo, T. Ambrizzi, J. D. Glick, 1999: Submonthly convective variability over and the Convergence Zone. J. Climate,12, 1877–1891. Madden, R. A. , and P. R. Julian, 1994: Observations of the 40–50 day tropical oscillation: A review. Mon. Wea. Rev. ,122, 814–837. Nogues-Paegle J. , K. Mo, and J. Paegle, 1998: Predictability of the NCEP/NCAR reanalysis model during austral summer. Mon. Wea. Rev. ,126, 3135–3152. Schemm, J. E. , H. Van den Dool, and S. Saha, 1996: A Multi-at NCEP. Preprints 11th Conf. on. Meteor. Soc. , 47–49. Size Fig. 1. Frequency response of Lanczos band-pass filters. The 30–70-day band is denoted intraseasonal type 1 (IS1), while variations of 10–30 days are designated intraseasonal type 2 (IS2). Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 2. Mean geopotential height at 500 hPa (Z500) during austral summer (top). [SEP] Justify:

Output: The text does not address the claim regarding microclimates and their effects on weather variations. Instead, it contains references to various studies and methodologies related to meteorology but lacks any explicit mention or discussion of microclimates or their impact on 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: Consistent with this, no such dominant negative phenotype is evident in a catalytically inactive PDE4A construct engineered to lack such an N-terminal targeting region. Our data contradict the synaptic homeostasis hypothesis for sleep function. This hypothesis proposes that sleep functions to downscale synaptic strength that has increased as a result of neuronal activity and experiences during wakefulness (Tononi and Cirelli, 2006). This hypothesis has focused on explaining data from the cortex rather than the hippocampus, but one previously published study has suggested that the synaptic homeostasis hypothesis applies to the hippocampus as well (Vyazovskiy et al. , 2008). However, the hippocampus may be unique from the cortex as the hippocampus is involved in episodic memory and in much greater experience-dependent plasticity than anywhere else in the brain and thus our findings may not extend to other areas where synaptic plasticity is not as prominent. Further, the hippocampus also exhibits many distinct forms of synaptic plasticity. Here, we examine structural changes in hippocampal neurons and find that extended wakefulness leads to a loss of synaptic spines mediated by a signaling pathway involving cofilin. This suggests that prolonged wakefulness down-regulates synaptic connectivity in the hippocampus. As little as 3 hr of recovery sleep is sufficient to restore signaling through these complexes, suggesting that sleep functions to restore synaptic connectivity. Thus, the signaling pathways that mediate changes in dendritic structure are rapidly impaired by sleep loss and then can be quickly restored during recovery sleep. Lack of sleep is a common problem in our 24/7 modern society and it has severe consequences for health, overall wellbeing, and brain function (Bryant et al. , 2004; Harrison and Horne, 2000). Despite decades of research, the mechanisms by which sleep loss negatively impacts brain function have remained unknown. Our findings suggest that the cognitive impairments caused by brief sleep deprivation are a result of altered spine dynamics leading to a reduction in spine numbers. Our findings may also explain the reduction in hippocampal volume observed in an animal model of more chronic sleep restriction (Novati et al. , 2011) and sleep disorders, such as primary insomnia (Riemann et al. , 2007) as well as sleep apnea (Morrell et al. , 2003). Our work defining the molecular pathway through which sleep deprivation impacts memory consolidation underscores the importance of the plasticity of the neuronal cytoskeleton and reveals that rapid synaptic remodeling occurs with changes in behavioral state. [SEP] Justify:

Output: The text corroborates the claim by explicitly stating that 'our findings suggest that the cognitive impairments caused by brief sleep deprivation are a result of altered spine dynamics leading to a reduction in spine numbers.' This directly supports the assertion that sleep deprivation negatively impacts cognitive functions.


Input: Task: contrast the claim using the given text. Claim: Biodiversity offsetting can completely reverse all environmental damage from development. [SEP] Text: This creation of ecologically equivalent credits is the defining characteristic of offsetting. The exchange between ecological gains and losses across space and time occurs in all the common approaches for delivering offsets, namely bespoke, project-specific offsets and conservation (or habitat) banking, but its potential to transform ecological credits into assets becomes most evident in the latter (Madsen et al. , Reference Madsen, Carroll, Kandy and Bennett2011). Conservation banking allows developers to buy credits (representing species or habitats) in order to either use them for internal mitigation (purchasing their right to degrade nature) or sell them to others (or both). This establishes a market for developers’ compensation liabilities (eftec, IEEP et al. , 2010; E. Apostolopoulou & W. M. Adams, unpubl. data), and allows credit purchasers to be involved in a for-profit version of conservation (Sullivan, Reference Sullivan2013b). Environmental offsetting is further advanced in the context of carbon than in that of biodiversity. Unlike carbon, biodiversity is always tied to place, making trade more problematic in technical terms, and more questionable in its principles than it already is for carbon (e. g. Bumpus & Liverman, and Liverman2008; Lohmann, Reference Lohmann2012). To overcome this and make biodiversity more accountable, marketable and tradable, there are attempts to create global units for biodiversity exchange, such as the Areas registry (VCA, 2015), which will list areas where biodiversity and ecosystem services are certified to be protected or restored ‘much as houses are listed on a real estate board’ (Hamrick, Reference Hamrick2014). Offsetting therefore reframes conservation practice around environmental markets, with monetary payments for biodiversity credits after the model of carbon trading. The resulting arrangements owe little to ecology. Ecosystems can be said to deliver bundles of services, and these can be stacked (or paid together to the landowners) or disaggregated. Robertson (Reference Robertson2012) points out that an interlocking set of ecological relations in a freshwater ecosystem might be defined as salmonid habitat credits and temperature credits and sold separately to interested buyers in other areas to compensate for their environmental impacts. Such a deal makes no ecological sense. In theory, the price of conservation credits should reflect the marginal cost of securing an offset (Conway et al. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and complexities of biodiversity offsetting. It indicates that while offset credits can be bought and sold, they 'owe little to ecology' and that arrangements made for offsets 'make no ecological sense' as ecosystems cannot be simply compensated for their degradation through market transactions. This suggests that biodiversity offsetting does not guarantee complete reversal of environmental damage, undermining the claim.


Input: Task: pass the claim using the given text. Claim: Microclimates can create variations in weather over short distances. [SEP] Text: Contour interval is 20 m. Total standard deviation of Z500 (bottom). Contour interval is 5 m. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 3. Anomaly correlation between Z500 forecasts and verification fields. Skill is computed considering all days from the four austral summers during the DERF period. Skill is shown for 1, 4, 6, 8, and 11 days lead time. Correlations greater than 0. 50 are indicated by dark shading. Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 4. (a) Mean outgoing longwave radiation (OLR) during austral summer (1 November–31 March). Contour interval is 5 W m−2 and shading indicates OLR is less than 220 W m−2. (b) Total OLR standard deviation during austral summer. Contour interval is 2. 5 W m−2 and shading indicates standard deviation greater than 20 W m−2. (c) Standard deviation of OLR anomalies for the IS1 type (30–70 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 10 W m−2. (d) Standard deviation of OLR anomalies for the IS2 type (10–30 days). Contour interval is 1. 0 W m−2 and shading indicates standard deviation greater than 15 W m−2. Region indicated with thick solid lines is used to characterize periods of strong (weak) convective activity over the (SACZ). Citation: Review 128, 2; 10. 1175/1520-0493(2000)128<0486:>2. 0. CO;2Download FigureDownload figure as PowerPoint slide Size Fig. 5. Distribution of events (in pentads) selected according to the type of intraseasonal variability: 30–70 days (IS1; top) and 10–30 days (IS2; bottom). Events are selected when the magnitudes of the OLR anomalies averaged over the SACZ domain are less than 0. [SEP] Justify:

Output: The text does not address the claim about microclimates and their impact on weather variation. Instead, it focuses on meteorological data and observations related to various measurements (like standard deviation and outgoing longwave radiation) without mentioning the concept of microclimates or how they affect local weather.