Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: The weight of the external forcing becomes particularly apparent if one examines the average evolution of Arctic sea ice coverage in the 100 member ensemble as a function of cumulative anthropogenic CO2 emissions (Fig. 2d). The long-term evolution of the sea ice cover directly follows the cumulative emissions. In particular, there is no clear difference in mean sea ice coverage between RCP 2. 6 and RCP 4. 5 for any given cumulative CO2 emission. We have recently been able to explain this relationship, which is largely based on the fact that the position of the outer edge of the sea ice cover is determined by the net local energy balance (Notz and Stroeve, 2016). Any rise in atmospheric CO2 concentration increases the incoming longwave radiation at the ice edge, causing the latter to move northward to a region with less incoming shortwave radiation. For geometric reasons, these combined processes lead to a roughly linear relationship between Arctic sea ice loss and anthropogenic CO2 emissions in the observational record and in all CMIP5 model simulations. In the observations, about 3 m2 of sea ice are lost per ton of CO2 emissions, while the models on average only simulate an ice loss of 1. 7 m2 per ton of CO2 emissions. The fact that the relationship between cumulative CO2 emissions and Arctic sea ice area is roughly linear strongly suggests a dominating role of the CO2 emissions for the evolution of sea ice area. However, other external drivers are also apparent, in particular in the mean across all simulations of our 100-member ensemble. Most striking are temporary increases in Arctic sea ice area following large volcanic eruptions during the historical period, most recently in 1991 after the Pinatubo eruption, in 1982 after the eruption of and in 1963 after the eruption of (compare also Zanchettin et al. , 2014). Because of the large internal variability and the relatively short-lived response, these eruptions are impossible to identify in the temporal evolution of individual simulations nor the observational record, but they apparently have contributed to a synthetic improvement of CMIP5 sea ice simulations relative to CMIP3 sea ice simulations (Rosenblum and Eisenman, 2016). short overview presents some recent work on the variability and long-term evolution of Arctic sea ice area. For space constraints, the focus was only on September sea ice coverage as this is the month with the strongest observed trends. [SEP] Justify:

Output: The text contrasts the claim by discussing the ongoing effects of anthropogenic CO2 emissions on Arctic sea ice coverage. It mentions that the long-term evolution of sea ice is closely linked to cumulative emissions, implying that previous melting has long-term effects on ice area. Furthermore, it highlights the linear relationship between CO2 emissions and sea ice loss, indicating that significant impacts from melting still persist rather than recovery.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: November 01 2009 Human-generated sound and marine mammals Loud anthropogenic noises can alter the behavior of whales and other marine mammals, sometimes with fatal consequences. Peter L. L. for other works by this author on: PubMed 10. 1063/1. 3265235 Open the PDF for in another window and. Tyack; Human-generated sound and marine mammals. 1 November 2009; 62 (11): 39–44. Download citation file: Ris (Zotero) toolbar search Menu toolbar search search input Search input auto suggest filter your search species of large whales are endangered because for centuries whaling fleets have decimated their populations. In the late 1960s, marine-mammal biologists discovered that fishermen setting nets for tuna in the were killing more than 100 000 dolphins a year. The cause of marine-mammal conservation became so popular at the dawn of the environmental movement that one of the first environmental accomplishments of the US Congress was to enact the of 1972, which prohibits the killing or injuring of marine mammals. Today, small remnant populations of whales, such as the right whale, are threatened by entanglement in fishing gear and collisions by ships. Indeed, marine biologists have estimated that hundreds of thousands of marine mammals are killed each year in fishing gear. 1 Inadvertent effects of human activities can pose a serious risk to coastal populations, as evidenced by the recent extinction of the Chinese river dolphin due to fishing, pollution, and overdevelopment of the Yangtze River. A few decades ago, conservation efforts focused on reducing the intentional hunting of marine mammals. Nowadays, when hunts for marine mammals are better controlled, the slow degradation of habitat from a combination of sources may have a bigger impact. For example, biologists have documented cases in which the effects of coastal development—including noise, pollution, and dredging—have caused marine mammals to abandon critical breeding habitat. 2 Noise in particular is at issue in legal actions that have been brought against the US Navy for sonar exercises that may have caused whales to strand and die (see Physics Today, February 2008, page 23). Behavior modification Payne and ocean engineer were the first to raise the alarm about the effect of sound on marine mammals. 3 In 1971 they considered the then recently discovered low-frequency calls associated with the reproduction of baleen whales. [SEP] Justify:

Output: The text does not address the claim about marine mammals communicating in complex human-like languages. Instead, it discusses the impacts of human-generated noise on marine mammals and conservation efforts for these species. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: Introductiontabaci and Trialeurodes vaporariorum (Hemiptera: Aleyrodidae) are notorious sap-sucking pests causing serious damage to vegetable crops through direct feeding and transmission of several plant viruses [1]. Tomato (Solanum lycopersicum) is one of the most favorable hosts of B. tabaci and T. vaporariorum [2], and these whitefly species cause tomato yield losses from 50 to 100% [3]. The control of whitefly is mainly dependent upon chemical pesticides to reduce agronomic losses [4, 5]. However, the use of chemical pesticides induces pest resistance and an outbreak of secondary pathogens [6]. Entomopathogenic fungi (EPF) Beauveria bassiana and Metarhizium anisopliae are the most environmentally-friendly bio-control agents against sucking insect pests [7]. They produce adhesion factors, cuticle degrading enzymes, infection structures [8], and toxic secondary metabolites to overcome host cuticles and cause infection [9]. These typical features of B. bassiana and M. anisopliae provide an advantage to effectively control sap-sucking insect species over others [10]. Different laboratory and field studies demonstrated that isolates of B. bassiana and M. anisopliae effectively controlled B. tabaci and T. vaporariorum with mortality ranging from 71 to 96. 61% [11,12,13]. Increased interests in the use of entomopathogenic fungi in pest management options necessitate the selection of fungal isolates with high virulence that shows significant enzyme activities on target insects. In Ethiopia, locally isolated entomopathogenic fungi showed promising results for the control of agricultural pests such as Aphis gossypiin [14], Pachnoda interrupta [15], and Tuta absoluta [16]. However, there is a limited report on the use of these native isolates for the management of whiteflies. Hence, this study was carried out to evaluate the enzymatic activity and pathogenicity competence of B. bassiana and M. anisopliae against whitefly species, B. tabaci and T. vaporariorum in Ethiopia. Main textMaterial and methodsIndigenous entomopathogenic fungi B. bassiana and M. anisopliae were used in trials (Additional file 1: Table S1). [SEP] Justify:

Output: The text corroborates the claim by stating that 'Entomopathogenic fungi (EPF) Beauveria bassiana and Metarhizium anisopliae are the most environmentally-friendly bio-control agents against sucking insect pests' and mentions laboratory and field studies demonstrating that B. bassiana effectively controlled pests like B. tabaci and T. vaporariorum.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: The discussion can be summarized as follows: - On seasonal time scales, atmospheric internal variability and its imprint on sea ice renders skillful predictions of September sea ice coverage more than two months in advance inherently difficult. - On annual time scales, negative feedbacks stabilize the sea ice cover. There is no “tipping point” beyond which the loss of the remaining summer sea ice becomes unstoppable. - On decadal time scales, internal climate variability can cause a substantial acceleration or temporary recovery of the sea ice cover that renders the evaluation of individual model simulations based on their short-term trends impossible. - On longer time scales, internal variability causes a substantial spread in possible 30-year long trends supporting for the production of large model ensembles. Nevertheless, the impact of anthropognic forcing on the long-term sea ice evolution is clear, with an average loss of 3 m2 of September sea ice cover per metric ton of anthropogenic CO2 emission. affiliation Institute for Meteorology, Hamburg, Germany 10. 1175/JCLI-D-11-00466. 1. Bitz, C. , and G. Roe, 2004: A mechanism for the high rate of sea ice thinning in the Arctic Ocean. J. Clim. , 17 (18), 3623–3632. . Blanchard-Wrigglesworth, E. , K. C. Armour, C. M. Bitz, and E. DeWeaver, 2011: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations, J. Climate, 24, 231–250. Blanchard-Wrigglesworth, E. , and Coauthors, 2016: Multi-model seasonal forecast of Arctic sea ice: forecast uncertainty at pan-Arctic and regional scales. Clim. Dyn. , 1–12, 10. 1007/s00382-016-3388-9. Bushuk, M. , D. Giannakis, and A. J. Majda, 2015: Arctic sea ice reemergence: The role of large-scale oceanic and atmospheric variability, J. Climate, 28, 5477–5509. Bunzel, F. , D. Notz, J. Baehr, W. A. Müller, and K. [SEP] Justify:

Output: The text contrasts the claim by discussing the complex dynamics of Arctic sea ice over various time scales, emphasizing that the impact of anthropogenic forcing on long-term sea ice evolution is clear and indicates an average loss of September sea ice cover. It mentions that while there might be temporary recovery or variability, the overall trend shows a loss, which directly rebut the claim that the majority of ice has recovered and suggests long-term negative effects from previous melting.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: A small industry has sprung from the observation that marine mammals may avoid loud sounds. Acoustic harassment devices are typically electronic sound sources designed to deter seals from catching farmed fish or to keep marine mammals away from such dangers as nets. The devices do not always deter seals, which may even interpret their loud sound as a dinner bell. The noises may be more likely to repel more sensitive animals such as the harbor porpoise. Many studies of porpoises in the wild and in the lab show that the animals avoid low levels of sound exposure at much greater distances than do seals. Figure 1, for example, shows tracks of harbor porpoises in British Columbia, Canada, observed in control conditions and when they were exposed to short, 145-dB sounds. 4 The porpoises avoided coming within a few hundred meters of the pinger; that is, they shied away from SPLs greater than about 100 dB. For fisheries where porpoises entangle in nets and die, pingers can be attached to the nets to reduce the number of animals killed. On the other hand, loud acoustic harassment devices may prevent porpoises, whales, and other cetaceans from using large swatches of their preferred habitat. View largeDownload slide Figure 1. Harbor porpoises avoid loud noises. In these plots, the white area is a part of the in British Columbia, Canada; black lines indicate porpoise tracks; and the red bits indicate float lines from which an electric pinger was suspended. (a) In this control run, the pingers are off. (b) When the pingers are on, they emit short noises with a sound pressure level of 145 dB relative to 1 µPa. The gap in tracks shows that porpoises avoid coming within a few hundred meters of the pingers. View largeDownload slide Figure 1. Harbor porpoises avoid loud noises. In these plots, the white area is a part of the in British Columbia, Canada; black lines indicate porpoise tracks; and the red bits indicate float lines from which an electric pinger was suspended. (a) In this control run, the pingers are off. (b) When the pingers are on, they emit short noises with a sound pressure level of 145 dB relative to 1 µPa. The gap in tracks shows that porpoises avoid coming within a few hundred meters of the pingers. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the behavioral responses of marine mammals, particularly harbor porpoises, to loud noises and acoustic harassment devices. It discusses how these animals avoid certain sound levels but does not address whether marine mammals communicate in complex languages akin to humans.


Input: Task: corroborate the claim using the given text. Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: The bioassay evaluation was tested by means separated using Tukey’s HSD test at P < 0. 05. The lethal time (LT50) and the lethal concentration (LC50 and LC90) values were determined with probit analysis (statics 20) [26]. ResultsCuticle degrading enzymatic activitiesEntomopathogenic fungi were showed significant differences in their relative enzyme activities ranging from 1. 20 to 3. 41 for chitinase, 1. 58 to 4. 45 for lipase, and 1. 72 to 5. 44 for protease (Table 1). On average, the isolates displayed the highest protease index (3. 41), followed by the lipase index (2. 86) and chitinase index (2. 42), respectively. Isolates showed significant differences in their enzyme activities; where almost all isolates (92%) showed excellent protease activities while 75% and 50% of the isolates displayed excellent lipase and chitinase activities respectively (Additional file 1: Fig. S1). Although 67% of the isolates showed excellent overall activities, B. bassiana AAUMB-29 performed best in chitinase activity (EI = 3. 41), whereas B. bassiana -77 and B. bassiana AAUEB-59 exhibited the highest lipase (EI = 4. 45), and protease activity (EI = 5. 44) respectively. Table 1 The enzymatic indices of B. bassiana and M. anisopliae isolatesFull size tableVirulence of B. bassiana and M. anisopliae isolates against whitefly adultsAll isolates were pathogenic to whiteflies, B. tabaci, and T. vaporariorum adults (Fig. 1). The different fungal isolates showed mortality of whitefly adults between 58 to 94. 27% for B. tabaci and 59. 03 to 95. 37% for T. vaporariorum after 10 days treatment. The result showed that M. anisopliae AAUDM-43 and B. bassiana -77 displayed the highest mortality of 94. 27% and 95. 37% on B. tabaci and T. vaporariorum adults, respectively (Additional file 1: Figs. S2, S3). Fig. 1Percent mortality (Mean ± SE) of B. [SEP] Justify:

Output: The text corroborates the claim by detailing how B. bassiana, among other fungal isolates, was pathogenic to whiteflies, specifically stating that it displayed mortality rates of 94.27% and 95.37% on whitefly species B. tabaci and T. vaporariorum, respectively. This supports the use of B. bassiana as a biocontrol agent against these pests.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Fröhlich, 2016: Seasonal climate forecasts significantly affected by observational uncertainty of Arctic sea ice concentration. Geophys. Res. Lett. , 43 (2), 2015GL066 928, 10. 1002/2015GL066928. Fetterer, F. , K. Knowles, W. Meier, and M. Savoie, 2002, updated 2016: Sea ice index. Digital media, and. Germe, A. , M. Chevallier, D. S. y Mélia, E. Sanchez Gomez, and C. Cassou,2014, Interannual predictability of Arctic sea ice in a global climate model: Regional contrasts and temporal evolution, Clim. Dyn. , 43(9-10), 2519–2538. Notz, D. , 2009: The future of ice sheets and sea ice: Between reversible retreat and unstoppable loss. Proc. Nat. Ac. Sci. , 106 (49), 20 590–20 595, doi:10. 1073/pnas. 0902356 106. Notz, D. , 2015: How well must climate models agree with observations? Phil. Trans. R. Soc. A, 373, 20140 164, 10. 1098/rsta. 2014. 0164. Notz, D. , A. Haumann, H. Haak, J. Jungclaus, and J. Marotzke, 2013: Arctic sea ice evolution as modeled by MPI-ESM. J. Adv. Model. Earth Syst. , 5, 173–194, 10. 1002/jame. 20016. < Notz, D. , and J. Marotzke, 2012: Observations reveal external driver for arctic sea ice retreat. Geophys. Res. Lett. , 39 (8), L051 094, 10. 1029/2012GL051094. Notz, D. , and J. Stroeve, 2016: sea ice loss directly follows anthropogenic CO2 emission. Science, aag2345, 10. 1126/science. aag2345. Pithan, F. , B. Medeiros, and T. Mauritsen, 2013: Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions. [SEP] Justify:

Output: The text contrasts the claim by providing references to studies that indicate ongoing issues with Arctic sea ice. For example, Notz (2016) points out that 'sea ice loss directly follows anthropogenic CO2 emission,' suggesting that there are significant long-term effects and that recovery is not occurring. Additionally, other sources mention the implications of observational uncertainties and model predictions for future ice concentrations, highlighting that the situation is still dynamic and problematic rather than exhibiting recovery.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: Close modal First, do no harm The use of intense deterrent sound sources highlights the need to define what exposures to underwater sound might harm a marine mammal. By the early 1970s, scientists had collected some data on effects of explosives on terrestrial mammals—for example, sheep submerged in a pond—that could help define levels that ruptured eardrums or caused injury to the lungs or other organs. Let alone the gruesomeness of the experiments, which I doubt would pass today’s animal welfare standards, submerged terrestrial mammals are not good proxies for marine mammals. In any event, as concern about the effects of sound on marine mammals increased in the early 1990s, it became clear that not enough data were available to define safe-exposure criteria for marine mammals. During the 1990s the US Office of supported the development of new methods to define levels of sound exposure that affect hearing in captive marine mammals. Those approaches take their cue from studies on humans. Extensive research in the workplace suggests that a useful standard is to limit noise exposure to below levels that affect our hearing. Although peak sound level can be an important indicator for risk of injury, particularly for impulse noise, sound exposure in the workplace is typically integrated over an eight-hour workday. That integrated sound exposure level (SEL) is a better overall predictor of risk than the SPL. The basic concept is to measure the faintest sound an animal can hear, then expose the animal to a noise stimulus and retest hearing. Measuring the noise just loud enough to cause a temporary reduction in hearing sensitivity gives a conservative estimate of the exposure that could pose a risk of injury if sustained or increased. In the mid-1990s, studies began to report exposures leading to temporary threshold shift (the term of art for temporary hearing loss) in captive seals and dolphins. By 2007 and colleagues had gathered enough data from TTS experiments with marine mammals and from studies of other species to establish criteria for acoustic injury. 5 For whales and dolphins, the criteria set a maximum 0-to-peak pressure level of 230 dB, a maximum SEL of 198 dB for pulsed sounds, and a maximum SEL of 215 dB for nonpulsed sounds. Data from seals suggest that their auditory systems may be affected by lower levels of sound; criteria for them are a maximum 0-to-peak pressure level of 218 dB and maximum SELs of 186 dB for pulsed sounds and 203 dB for nonpulsed sounds. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the effects of sound exposure on marine mammals and the development of criteria for acoustic injury, rather than addressing the complexity or nature of communication among marine mammals. There is no mention of language or communication in the context of the claim.


Input: Task: corroborate the claim using the given text. Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: tabaci and T. vaporariorum adults after 10 days of treatment with M. anisopliae and B. bassiana at 1 × 107 conidia/mlFull size imageMortality (%), median lethal time (LT50), and spore production perspective of isolates on cadavers of whitefly nymphsThe bio-insecticide efficacies of entomopathogenic fungi showed significant differences (P < 0. 001) in percentage mortality of B. tabaci and T. vaporariorum nymphs (Table 2). The mortality of B. tabaci and T. vaporariorum nymphs varied from 71. 67 to 98. 33% and 60 to 100%, respectively. Thus, B. bassiana AAUMB-29 displayed 98. 33% mortality on B. tabaci. Similarly, B. bassiana AAUMB-29 and -77 caused 100% mortality on T. vaporariorum nymphs. Table 2 Mortality (%), median lethal time (LT50), and spore production per B. tabaci and T. vaporariorum nymphs after 10 days of treatment with M. anisopliae and B. bassiana at 1 × 107 conidia/mlFull size tableConcerning lethal time at 50% (LT50) mortality values, isolates fell within the range of 3. 22 to 9. 26 days for B. tabaci and 3. 08 to 8. 16 days for T. vaporariorum nymphs (Table 2). B. bassiana -77 achieved the least LT50 values of 3. 22 days and 3. 08 days on B. tabaci and T. vaporariorum respectively. The spore production of isolates on whitefly nymph cadavers varied significantly among isolates (P < 0. 001) with profuse sporulation ranging from 1. 3 × 105 to 6. 5 × 106 on B. tabaci and 1. 6 × 105 to 5. 8 × 106 conidia/cadaver on T. vaporariorum. Regarding the multiple dosage (1 × 105–1 × 108 conidia/ml) response evaluations, the B. bassiana AAUMB-29 revealed the lowest LC50 values of 6. 8 × 104 conidia/ml on T. [SEP] Justify:

Output: The text corroborates the claim by providing evidence of the efficacy of Beauveria bassiana as a biocontrol agent against pests. It details the significant mortality rates caused by B. bassiana on targets such as B. tabaci and T. vaporariorum, indicating its effectiveness in pest management.


Input: Task: pass the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Holland, 2011b: Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys. Res. Lett. , 38, L18503, falseSearch 1505. 05424. Bonan, D. B. , M. Bushuk, and M. Winton, 2019: A spring barrier for regional predictions of summer Arctic sea ice. Geophys. Res. Lett. , 46, 5937–5947, D. B. , M. Bushuk, and M. Winton, 2019: A spring barrier for regional predictions of summer Arctic sea ice. Geophys. Res. Lett. , 46, 5937–5947, falseSearch 10. 1175/1520-0442(1992)005<0541:>2. 0. CO;2. CrossrefBretherton, C. S. , C. Smith, and J. M. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5, 541–560, falseSearch 10. 1002/2017GL073155. CrossrefBushuk, M. , R. Msadek, M. Winton, G. A. Vecchi, R. Gudgel, A. Rosati, and X. Yang, 2017: Skillful regional prediction of Arctic sea ice on seasonal timescales. Geophys. Res. Lett. , 44, 4953–4964, falseSearch 10. 5194/npg-27-373-202. CrossrefChattopadhyay, A. , P. Hassanzadeh, and D. Subramanian, 2020: Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: Reservoir computing, artificial neural network, and long short-term memory network. Geophys. , 27, 373–389, falseSearch 10. 1007/s00382-018-4592-6. CrossrefCruz-García, R. , V. Guemas, M. Chevallier, and F. Massonnet, 2019: An assessment of regional sea ice predictability in the Arctic Ocean. Climate Dyn. , 53, 427–440, falseSearch 10. 1002/qj. 828. CrossrefDee, D. [SEP] Justify:

Output: The text does not address the claim concerning the recovery of Arctic ice or the long-term effects of previous melting. It mainly cites various studies about Arctic sea ice without providing any information that corroborates or contrasts the claim.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: According to current US regulations, SPLs above 180 dB pose a risk of injury to whales and dolphins. Despite seals’ apparently greater sensitivity to noise exposure, regulations set 190 dB as the threshold for risk of injury to them. US regulations also establish criteria for disrupting behavior. They set the disruption threshold at 160 dB for whales and dolphins. The threshold for porpoises is lower—120 dB—because of evidence that they respond to sounds at lower levels than many other cetacean species. Note that the US regulations are in terms of SPL, a different measure from the 0-to-peak pressure and SEL used for the criteria suggested by Southall and company. The sound produced by air-gun arrays, explosives, and some sonars is so intense that it could directly injure animals in the immediate vicinity. US regulations require that such sound sources be shut down if a marine mammal might enter the zone of potential injury. The standard oil-industry practice for reducing unintentional exposure during seismic surveys is to start firing just one air gun and slowly increase the number of guns until the full array is firing. Once the array is operating, regulators assume, whales will swim out of the danger zone. So the survey ships are allowed to operate at night and in fog when it is impossible to sight animals. Dtag, you’re it The assumptions behind noise regulations have a Goldilocks feel to them—whales will respond to air guns strongly enough to stay out of danger but not so strongly as to significantly disrupt their behavior. And as discussed above, evidence exists that baleen whales, such as the bowhead and gray, reliably avoid air guns. But by 2000 it was clear that empirical evidence could not justify the assumption that toothed whales—related to but distinct from the baleens—moved out of a danger zone as a seismic survey vessel either ramped up an air-gun array or approached with a full array firing. Indeed, sperm whales tend to be sighted closer to seismic vessels when air guns are active. 6 The lack of definitive data for toothed whales was not lost on the US Service (MMS), which is responsible for assessing the impacts of its leases of offshore tracts for oil development. As the oil industry moved into the deep-water habitat of sperm whales, which the US lists as endangered, the MMS was stimulated to support research on how sperm whales in the Gulf of Mexico respond to air guns. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses regulations regarding sound levels that pose risks to marine mammals and their behavioral responses to noise, without addressing the complexity of their communication systems. There is no mention or evidence supporting or refuting the notion that marine mammals communicate in languages akin to humans.


Input: Task: corroborate the claim using the given text. Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: vaporariorum, (Additional file 1: Table S2) and 2. 7 × 104 conidia/ml on B. tabaci (Additional file 1: Table S3). Isolate B. bassiana -77 achieved the lowest LC90 value of 1. 9 × 106 conidia/ml against B. tabaci, whilst B. bassiana AAUMB-29 exhibited the lowest (1. 5 × 106 conidia/ml) against T. vaporariorum. DiscussionThe main important bio-insecticidal traits of entomopathogenic fungi are the production of cuticle degrading extracellular enzymes [27]. Consequently, isolates of B. bassiana, and M. anisopliae were produced chitinase, lipase, and protease enzymes. The data showed that isolates differed in their chitinase, lipase, and protease enzyme activities that are attributed to their intraspecific and interspecific variability [28]. The three potential isolates, B. bassiana AAUMB-29, B. bassiana -77, and M. anisopliae AAUDM-43 displayed a high level of chitinase, lipase, and protease activities. Hence, the greater chitinase, lipase, and protease activities of isolates indicate the capability of protein, chitin, and lipids breakdown by these isolates. This alludes to that the fungal isolates are capable of successful penetration of insect cuticles [29, 30], with a high virulence effect against target insects [31]. In this study, the M. anisopliae AAUDM-43 induced the highest mortality of 94. 27% on B. tabaci whereas B. bassiana -77 inflicted greater mortality of 95. 37% on the T. vaporariorum adults after 10 days of treatment at the rate of 1 × 107 conidia/ml. Among the isolates, B. bassiana AAUMB-29 displayed the highest mortality (98. 33%) of nymphs on B. tabaci and both isolates of B. bassiana AAUMB-29 and B. bassiana -77 achieved 100% nymph mortality on T. vaporariorum. Several studies also confirmed the great bio-efficacy of B. bassiana and M. anisopliae against B. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that isolates of B. bassiana, such as B. bassiana AAUMB-29 and B. bassiana -77, show high mortality rates against pests like T. vaporariorum and B. tabaci. It details that B. bassiana -77 inflicted 95.37% mortality on T. vaporariorum, and B. bassiana AAUMB-29 displayed 98.33% mortality of nymphs on B. tabaci, demonstrating its effectiveness as a biocontrol agent.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: liu@esciencecenter. nl Abstractsea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time scales. Numerical models require near-real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely convolutional long short-term memory networks (ConvLSTM), to forecast sea ice in the at weather to subseasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence, and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is a promising way to enhance operational Arctic sea ice forecasting in the near future. Supplemental information related to this paper is available at the website: Denotes content that is immediately available upon publication as open access. This 4. 0 license ( 2021 Society. Corresponding author: Yang Liu, y. liu@esciencecenter. nl Keywords: Sea ice; Statistical forecasting; Deep learning; Machine learning1. IntroductionAs one of the most noticeable frontiers with visible changes due to global warming, the Arctic has received more and more attention in recent decades. This is accompanied with increased commercial and scientific activities as a result of sea ice melting. This drives a demand for reliable operational sea ice forecasts, especially for shipping companies and related stakeholders (Gascard et al. 2017; Stephenson and Pincus 2018). Therefore, it is of crucial importance to improve operational Arctic sea ice forecasts at weather time scales. The physical interactions between atmospheric and oceanic conditions and Arctic sea ice provide a basis for forecasting sea ice characteristics. [SEP] Justify:

Output: The text contrasts the claim by indicating that the Arctic has undergone noticeable changes due to global warming and that there is a demand for reliable operational sea ice forecasts as a result of sea ice melting. It implies that the impacts of previous melting are significant enough to necessitate improved forecasting, countering the claim that there are no long-term effects.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: Until relatively recently, we who study how sound affects marine mammals have been hampered by primitive methods of measuring an animal’s behavioral responses and acoustic exposure. I have worked with engineer to develop a tool to solve the problem—a tag that can be placed on the animal to record sound and movement. (digital tag), shown in figure 2 and on this month’s cover, uses three-axis magnetometers and accelerometers to measure the orientation and movement of a whale, a pressure sensor to measure depth of dive, and calibrated hydrophones to measure sound at the whale. The tag is attached to a whale with suction cups and records behavioral data at a high rate to flash memory. It is programmed to detach from the whale after up to 18 hours, during which time it collects up to about 10 gigabytes of data. After detachment, a radio beacon guides scientists to the tag, so that they can download the data. Not only can the Dtag record subtle details of behavioral responses, but it also acts as an acoustic dosimeter, recording sounds—including anthropogenic sounds—as heard by the whale. View largeDownload slide Figure 2. A whale gets fitted with a digital tag. (a) Mark Johnson, design engineer of the Dtag, holds a tag in its housing. Pointing toward the viewer are the suction cups used to attach the tag to the whale. Extending to the upper right is the antenna for the radio beacon used to find the tag once it releases from the whale. (b) Marine biologists use a carbon fiber pole to attach a tag to a pilot whale. (c) A beaked whale displays a Dtag. View largeDownload slide Figure 2. A whale gets fitted with a digital tag. (a) Mark Johnson, design engineer of the Dtag, holds a tag in its housing. Pointing toward the viewer are the suction cups used to attach the tag to the whale. Extending to the upper right is the antenna for the radio beacon used to find the tag once it releases from the whale. (b) Marine biologists use a carbon fiber pole to attach a tag to a pilot whale. (c) A beaked whale displays a Dtag. Close modal Deep-diving toothed whales can remain out of sight for more than an hour and have been among the most difficult animals to observe in the wild. [SEP] Justify:

Output: The text does not address the claim about marine mammals communicating in complex human-like languages. Instead, it discusses the development of a digital tag for studying the behavioral responses and acoustic exposure of marine mammals like whales. There is no mention or implication of the complexity or nature of their communication.


Input: Task: corroborate the claim using the given text. Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: tabaci and T. vaporariorum, with mortality values of 86. 47 to 96. 61% at the concentration of 1 × 106 conidia/ml in Italy [13], 71 to 86% at 1 × 107 conidia/ml in Mexico [32]. The disparities in bio-control efficiency of isolates could be due to differences in conidial viability, spore concentrations, the vulnerability of pests, enzymatic activities, and experimental conditions [33, 34]. Spore production on the surface of target insect cadavers is one of the important parameters for the selection of candidate biological control agents. The B. bassiana -77 yielded the highest numbers of spores with 6. 5 × 106 conidia/cadaver on B. tabaci and 5. 8 × 106 conidia/cadaver on T. vaporariorum nymphs. These numbers were slightly higher than the number of spores produced; 7. 9 × 105 conidia/cadaver of whitefly nymph with B. bassiana [22], but lower than spore production by B. bassiana (8. 3 × 107 per beetle cadaver) [35]. The difference in spore production of fungal isolates on insect cadavers might be due to variation in humidity, fungal isolate, host species, experimental method, host stage, and body size [36]. With regard to the median lethal concentration, B. bassiana AAUMB-29 which was highly effective against the nymphs of whitefly species gave the lowest LC50 values of 6. 8 × 104 conidia/ml on T. vaporariorum (Additional file 1: Table S2) and 2. 7 × 104 conidia/ml on B. tabaci (Additional file 1: Table S3). The finding was slightly better than LC50 values on application with M. anisopliae and B. bassiana ranging from 0. 22 × 104 to 4. 91 × 106 conidia ml−1 against whitefly nymphs reported [37]. ConclusionThis particular study showed that B. bassiana and M. anisopliae indicated differences in the production of chitinase, lipase, and protease enzymes. The B. bassiana AAUMB-29, B. bassiana -77, and M. [SEP] Justify:

Output: The text corroborates the claim by providing specific details about the effectiveness of Beauveria bassiana (B. bassiana) as a biocontrol agent against pests, particularly mentioning its efficacy against Tetranychus vaporariorum and Bemisia tabaci. It discusses mortality values induced by B. bassiana at different concentrations, indicating its role in pest management. Furthermore, it references the spore production on insect cadavers, which is a critical factor in its effectiveness as a biological control agent.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Predictability of Arctic sea ice at different time scales in different seasons has been explored extensively in many studies. Blanchard-Wrigglesworth et al. (2011a) investigated the temporal evolution of Arctic sea ice in observations and in ensemble climate model output. They found a summer to summer and a melt season to growth season reemergence effect in sea ice which potentially serves as a good predictor for Arctic sea ice forecasts at monthly to annual time scales. Mohammadi-Aragh et al. (2018) studied the potential predictability of Arctic sea ice in winter, including the deformation and concentration of sea ice at weather time scales. They noticed that the sea ice concentration (SIC) is predictable throughout a 10-day forecast period. In addition there are studies on longer range forecasts. Guemas et al. (2016) reviewed progress on sea ice forecasts and showed that predictability of Arctic sea ice at seasonal to decadal time scales mainly originates from persistence or advection of sea ice anomalies, air–sea interaction, and changes in radiative forcing. Krikken and Hazeleger (2015) analyzed the natural variability of Arctic sea ice from an energy budget perspective. They found strong correlations between the Arctic energy balance components and the reemergence of sea ice anomalies from the melt season to the growth season, which extends the theory proposed by Blanchard-Wrigglesworth et al. (2011a) and further confirms the essential role of the energy budget in sea ice forecasts. Another key element to the predictability of sea ice is the sea ice thickness (SIT). Bonan et al. (2019) explore the role of SIT on the summer predictability of sea ice and found that similar skill of SIT forecasts can be obtained as a perfect model experiment. They also discussed the predictability barrier in late spring and made suggestions for the initialization of forecasts. Cruz-García et al. (2019) examined seasonal-to-interannual sea ice predictability with multiple climate models and revisited the essential role of the reemergence effect of sea ice anomalies. They observed that SIC anomalies in the have a strong negative correlation with the local sea surface temperature (SST) anomalies. Moreover, Onarheim et al. (2015) emphasized that ocean heat transport (OHT) variations play an important role in the observed winter sea ice variance in the Barents Sea. [SEP] Justify:

Output: The text contrasts the claim by discussing various studies on the predictability of Arctic sea ice, indicating that there are complex dynamics at play regarding ice recovery and persistence. It suggests that sea ice variability and long-term predictability are influenced by multiple factors, including air-sea interaction and energy balance, and implies that previous melting impacts the current state of sea ice rather than recovery being complete with no long-term effects.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: The y-axis gives the sound pressure level (SPL) relative to an arbitrary reference pressure. (b) In this echogram, the y-axis indicates the distance to the prey. More precisely, one measures half the time elapsed between an outgoing click and the returning echo, then multiplies by a presumed sound speed of 1500 m/s. The color scale indicates the energy of the signal: blue for an intense signal, yellow for a faint one. The black dashed curve is to guide the eye through relatively strong echoes from the target the whale was approaching. (c) Tagging can also indicate the magnitude of a whale’s dynamic acceleration. The increased acceleration during the buzz probably indicates movements associated with prey capture. Close modal Armed with a tag that could measure behavioral responses to measured dosages of sound at a whale, my colleagues and I were ready to study the effects of air guns on deep-diving sperm whales. One component of the (SWSS), sponsored by a number of governmental and industrial agencies, used small boats to tag sperm whales and then directed a seismic survey vessel to ramp up its air-gun array and conduct a controlled approach to the tagged whales. 9 In the course of five experiments, we obtained tracking information for seven whales. We found no evidence of avoidance behavior as the seismic vessel advanced toward the whales; indeed, the whale that was approached most closely rested near the surface for an unusually long time. Immediately after the air guns stopped, that whale started a foraging dive—apparently, it had waited to start foraging. The other six whales did conduct deep foraging dives during exposure to air guns. However, they reduced their swimming effort significantly and, judging from their diminished buzz rate, may have reduced their attempts to capture prey. As figure 4 shows, the reduction in buzz rate became more pronounced as the seismic vessel got closer to a whale. The data raise concern that the Goldilocks assumptions do not apply to toothed whales. No evidence suggests that the whales move away from approaching air guns, but there is evidence that the air guns disrupt their foraging behavior. View largeDownload slide Figure 4. Air guns disrupt the foraging behavior of sperm whales. The left-hand part of the plot shows how the buzz rate during air-gun exposure compares with the postexposure rate as a function of distance to a seismic vessel. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim as it discusses the effects of air guns on sperm whales and does not address the complexity or nature of marine mammal communication. It focuses on tracking information and behavioral responses related to foraging and sound exposure, without mentioning any aspect of language or complex communication systems.


Input: Task: corroborate the claim using the given text. Claim: Beauveria bassiana is used as a biocontrol agent against various pests. [SEP] Text: anisopliae AAUDM-43 were the most virulent against whitefly nymphs and adults. The whitefly nymphs were more vulnerable to infection with M. anisopliae and B. bassiana than the adult stages of the whitefly species. LimitationsThis study is limited to the enzymatic activities and bioassay study of B. bassiana and M. anisopliae against whiteflies under in-vitro conditions. A future study is required under field conditions to realize the efficiency of isolates for the development of myco-insecticide. Availability of data and materials The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. AbbreviationsAREA: Average relative enzymatic activity CL: Confidence limit E: Excellent activity EI: Enzymatic index F: Fair activity G: Good activity EPF: Entomopathogenic fungi LC50 : The median lethal concentration required to kill 50% P: Poor activity PDA: Potato dextrose agar REA: Relative enzymatic activity SE: Standard error 11. 70. 821. CAS 7. 17. CAS 360. 132. 64. 45. 30. 9. 9. 60. 12. 45. 4. 7091. 16. 2008. p. 157–67. 2. 18. 87. 7. 1984. 1971. 9. 502. 74. 64. 96. 50. 21. 99. 83. 21. 105. 1. Sources of Beauveria bassiana and Metarhizium anisopliae isolates used in this study. Table S2. The probit analysis of lethal concentrations values of B. bassiana and M. anisopliae in multiple dose-mortality response bioassays against T. vaporariorum nymphs 10 days post-fungal application. Table S3. The probit analysis of lethal concentrations values of B. bassiana and M. anisopliae in multiple dose-mortality response bioassays against B. tabaci nymphs 10 days post-fungal application. Figure S1. Extracellular activities of entomopathogenic fungi. Lipase activity of B. bassiana -77 (A), protease activity of B. bassiana AAUMB-29 (B), and Chitinase activity of M. [SEP] Justify:

Output: The text corroborates the claim by discussing the effectiveness of Beauveria bassiana (B. bassiana) as a biocontrol agent against pests, specifically mentioning its virulence against whitefly nymphs and adults. It states that the whitefly nymphs were more vulnerable to infection with B. bassiana than the adult stages, indicating its potential as a biocontrol agent for this pest.


Input: Task: pass the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Ingvaldsen, 2012: Quantifying the influence of Atlantic heat on Barents sea ice variability and retreat. J. Climate, 25, 4736–4743, falseSearch 4. Quart. J. Roy. Meteor. Soc. , 139, 1132–1161, M. A. , K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc. , 139, 1132–1161, falseSearch 1. 0. ERA Rep. Series 1, 16 pp. , E. , K. C. Armour, C. M. Bitz, and E. DeWeaver, 2011a: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J. Climate, 24, 231–250, E. , K. C. Armour, C. M. Bitz, and E. DeWeaver, 2011a: Persistence and inherent predictability of Arctic sea ice in a GCM ensemble and observations. J. Climate, 24, 231–250, falseSearch 10. 1029/2011GL048807. CrossrefBlanchard-Wrigglesworth, E. , C. Bitz, and M. Holland, 2011b: Influence of initial conditions and climate forcing on predicting Arctic sea ice. Geophys. Res. Lett. , 38, L18503, falseSearch 1505. 05424. Bonan, D. B. , M. Bushuk, and M. Winton, 2019: A spring barrier for regional predictions of summer Arctic sea ice. Geophys. Res. Lett. , 46, 5937–5947, D. B. , M. Bushuk, and M. Winton, 2019: A spring barrier for regional predictions of summer Arctic sea ice. Geophys. Res. Lett. , 46, 5937–5947, falseSearch 10. 1175/1520-0442(1992)005<0541:>2. 0. CO;2. CrossrefBretherton, C. S. , C. Smith, and J. M. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data. J. [SEP] Justify:

Output: The text does not address the claim directly or provide any supporting or contrasting information regarding the recovery of Arctic ice or the long-term effects of melting. Instead, it lists various studies related to Arctic sea ice variability, retreat, and prediction methods without discussing whether the ice has recovered or mentioning the long-term impacts of previous melting.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: The horizontal bars indicate the range of distances during air-gun exposure, and the vertical bars indicate the standard error of difference in buzz rate. The red data point highlights one animal, sw173b, that made no foraging dives while the air guns were firing and therefore made no buzzes. The right section of the figure compares buzz rates during and after no-sound control runs that mimicked exposure and postexposure conditions. View largeDownload slide Figure 4. Air guns disrupt the foraging behavior of sperm whales. The left-hand part of the plot shows how the buzz rate during air-gun exposure compares with the postexposure rate as a function of distance to a seismic vessel. The horizontal bars indicate the range of distances during air-gun exposure, and the vertical bars indicate the standard error of difference in buzz rate. The red data point highlights one animal, sw173b, that made no foraging dives while the air guns were firing and therefore made no buzzes. The right section of the figure compares buzz rates during and after no-sound control runs that mimicked exposure and postexposure conditions. Close modal Acoustic measurements Underwater sound is typically measured by sensing acoustic pressure—that is, the deviation from the average hydrostatic pressure. Typically, acoustic measures are given in units of decibels (dB) with respect to some reference. If ρ is a measured acoustic pressure and ρref a reference pressure, then the sound pressure level (SPL) in decibels with respect to the reference pressure is SPL = 20 log 10 ( p p ref ). So, for example, beaked whales have been observed to avoid SPLs of 136–140 dB re 1 µPa or greater. The reference pressure of 1 µPa is standard for underwater sound pressure measurements. With that understanding, I’ll assume pressures are measured in µPa and will drop the explicit accounting of the reference pressure. Short transient sounds are often described in terms of a peak pressure, while longer transients and continuous sounds are more typically expressed in terms of a root mean square (rms) pressure, p r m s = 1 T ∫ 0 T p 2 ( t ) d t where T is the averaging time. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the effects of air-gun exposure on the foraging behavior of sperm whales and the measurement of underwater sound pressure, without providing any information about the communication methods or linguistic complexity of marine mammals.


Input: Task: corroborate the claim using the given text. Claim: Nosema ceranae is a parasite that affects honeybee health. [SEP] Text: G5_1_1BCO2, S. marcescens, S. nematodiphila) and G. apicola were significantly enhanced towards siRNA treatment during the entire experimental period (P < 0. 001). Fig. 1Read counts of microbes during the experimental period. Reads aligned to the bee genome decreased over time and the reads aligned to microbes increased. Overall, the microbes were negatively correlated with N. ceranae proliferation. X axis represents days post infection. Y axis represents the relative abundance of the studied microbesFull size imageDiscussionWhen a parasite infects a host, infection success depends on host immune defenses, microbial community responses, and even competition from other parasites [25]. In turn, parasites spatially alter the relative abundance of gut microbes and other symbionts [26]. Honey bee gut microbes are important for food digestion and bee immune responses [17]. In honey bees, at least 406 bacterial species and variants have been found (based on the database), including eight bacterial clusters that form the core gut microbial community [16, 17]. Within this core set of bacteria, the symbionts Gilliamella apicola and Snodgrassella alvi support food digestion and pathogen defenses, even though sequence variance may lead to functional variation [27, 28]. In contrast, the bacterium Frischella perrara triggers the honey bee melanization response [29]. Honey bees and their gut bacteria both respond to stressors and infection. As two co-regulation groups were identified, the relative abundance of bacteria may not be independent of each other in the mid-gut tissue. Additionally, all of the studied bacteria were negatively correlated with N. ceranae proliferation, suggesting N. ceranae reduces bacterial abundance, at least for the studied 15 bacteria. In the gut microbiome, diet shapes bacterial diversity and responses towards N. ceranae infection, where a negative correlation between S. alvi and N. ceranae was found, which is consistent with our data [30]. When this microbial community was disturbed by antibiotics, honey bees were more susceptible towards N. ceranae and other opportunistic pathogens [8, 31]. Administration of Lactobacillus strains as food additives showed suppressive effects on N. ceranae spores [32]. Competition for limited food resources may lead to the decrease/exclusion of other competitors, as suggested by the competitive exclusion principle [33]. [SEP] Justify:

Output: The text corroborates the claim by discussing how N. ceranae reduces bacterial abundance in the honey bee gut and negatively correlates with bacterial proliferation. Additionally, it notes that when the honey bee microbiome is disturbed, the bees become more susceptible to N. ceranae, indicating that N. ceranae affects honeybee health by altering their gut microbiome and impacting their immune responses.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Preprints AbstractDiscussion © Author(s) 2022. This work is distributed under the Attribution 4. 0 License. © Author(s) 2022. This work is distributed under the Attribution 4. 0 License. Preprints AbstractDiscussionMetrics 02 Jun 2022 | 02 Jun 2022 Status: this preprint was under review for the journal TC but the revision was not accepted. Reconstruction of Arctic sea ice thickness and its impact on sea ice forecasting in the melting season and Abstract. Generally, the sea ice prediction skills can be improved via assimilating available observations of the sea ice concentration (SIC) and the sea ice thickness (SIT) into a numerical forecast model to update the initial fields of the model. However, due to the lack of SIT satellite observations in the melting season, only SIC fields in the forecast model can be directly updated, which will bring about the dynamical mismatch between SIC and SIT to affect the model prediction accuracy. In order to solve this problem, a statistically based bivariate regression model of SIT, named as BRMT, is tentatively established based on the grid reanalysis data of SIC and SIT, to reconstruct the daily Arctic sea ice thickness data. Both BRMT-constructed SIT and several popular reanalysis datasets are compared to each other and validated based on available SIT observations in situ. Results show that BRMT can effectively reproduce the spatial and temporal changes of ice thickness in the melting season, and BRMT-constructed SIT is more accurate in capturing the change trend of ice thickness over a period of time, also the reconstructed SIT of one-year ice and multi-year ice types in the central Arctic and E are closer to the observations. Further, as SIT from BRMT and SIC from satellite remote sensing are jointly assimilated into the ice-sea coupled numerical model, the prediction accuracy of SIC and SIT in the Arctic melting season is significantly improved, especially the SIC in the marginal ice zone and SIT in the central Arctic. How to cite. Yang, L. , Fu, H. , Luo, X. , Zhang, S. , and Zhang, X. : Reconstruction of Arctic sea ice thickness and its impact on sea ice forecasting in the melting season, Discuss. [preprint], 2022. [SEP] Justify:

Output: The text contrasts the claim by discussing the difficulties in predicting Arctic sea ice thickness and forecasting due to the lack of satellite observations during the melting season. It implies that changes in sea ice thickness and concentration are ongoing and that previous melting has long-term impacts that affect prediction accuracy, suggesting that the ice may not have recovered fully, which is in direct opposition to the claim.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: That realization led and me to hypothesize that beaked whales might show a strong anti-predator response to the sonar signals. The stimuli selected to test our idea were an actual naval sonar signal, calls of marine-mammal-eating killer whales, and a noise stimulus with the same overall frequency band and timing as the sonar signal but with a waveform that sounded very different from either sonar or a killer whale. The experimental design called for tagging a beaked whale and collecting preexposure data during the whale’s first one or two foraging dives. Then, as soon as echolocation clicks were detected in the next foraging dive, a ship was to start playing back one of the stimuli at a level so low that the whale could not hear it, then increase the source level regularly until it reached a maximum intensity (well under that of the sonar) or the whale stopped clicking. In the summer of 2007, the experimental team exposed a tagged whale to sonar during its second dive and to killer-whale calls during its third dive. The following summer a different tagged whale was exposed to the noise stimulus after two preexposure dives. In all three trials the whale stopped clicking in response to the sound stimulus and in particular produced fewer foraging buzzes than normal. The whale then did an unusually long, slow ascent, moving away from the source of the sound. After exposure to killer-whale calls, the beaked whale swam continuously for 10 hours along an atypically straight course out of the Tongue of the Ocean. According to our killer-whale hypothesis, given similar exposure levels, beaked whales would show a similar response to sonar and killer-whale calls. That response might differ from the one induced by noise stimulus. In fact, the beaked whales showed similar dive and clicking responses to all three stimuli. But the response to killer-whale calls occurred at a much lower received root mean square SPL, about 100 dB averaged over 0. 2 s, and was much stronger and more prolonged than those due to the other stimuli. The anthropogenic stimuli elicited cessation of clicking at received SPLs of about 140 dB, averaged over 0. 2 s. That intensity is similar to the 136-dB level of shipping noise that provoked a Cuvier’s beaked whale to break off a foraging dive in the Mediterranean Sea. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses an experimental study involving beaked whales' responses to sonar and killer-whale calls, but does not address or support the idea that marine mammals communicate in complex human-like languages. There is no analysis or mention of the complexity or nature of their communication systems.


Input: Task: corroborate the claim using the given text. Claim: Nosema ceranae is a parasite that affects honeybee health. [SEP] Text: Therefore, a nutritional context is essential when interpreting any responses of the bacterial community toward parasite challenges. In our data, parasites negatively affected the relative abundance of all 15 studied gut bacteria, leading to interrupted food digestion and nutrient absorption. It remains unclear whether gut bacteria can actively defend the integrity of the gut tissue in bees. However, microbes can be regulated by diet to protect the gut integrity and support pathogen resistance in other organisms [34]. In prior work, honey bee pathogens have shown negative correlations with S. alvi [35, 36]. From our data, the levels of F. perrara and S. alvi were significantly enhanced with the reduction of N. ceranae. It remains unclear why N. ceranae negatively affected gut bacteria. Even though sugar was the only food resource in our studied bees, the chance is low that N. ceranae directly competes for this food resource with bacteria, as N. ceranae proliferates within epithelia cells and bacteria live in the lumen. Tissue destruction caused by infection might lead to the deterioration of the gut lumen, thus impacting bacteria. Although microbial treatments provide a promising chemical-free strategy to control pathogen infection [37], caution is required, as improper probiotic supplements can lead to dysbiosis of gut microbes and susceptibility toward pathogens [38]. In our study, the relationship between N. ceranae and the studied microbes was correlative. However, the siRNA Dicer treatment and the reduced N. ceranae level was causative. We provided novel insights into interactions between gut parasites and other microbes over the entire life cycle of the parasite. Our study is limited to mid-gut tissues of newly-emerged workers. It will be interesting to screen N. ceranae and bacterial interactions in older forager bees, and in bees collected during different seasons. ConclusionsLevels of N. ceranae are negatively correlated with levels of gut bacteria. Gut bacteria were not independent with each other and were co-regulated. The relative abundance between the gut bacteria and N. ceranae was negatively correlated. MethodsEthics statementThree apiaries (with 10, 10, and 20 colonies in each apiary, respectively) for bee sample collection were established at the USDA-. No specific permits were required for the described studies. Studies involved the European honey bee (Apis mellifera mellifera), which is neither an endangered nor protected species. [SEP] Justify:

Output: The text corroborates the claim by discussing the negative effects of N. ceranae on gut bacteria in honeybees. It states that 'Levels of N. ceranae are negatively correlated with levels of gut bacteria,' indicating that the presence of this parasite disrupts the bacterial community essential for the bees' health. Additionally, it mentions that the parasite can impact food digestion and nutrient absorption, further supporting the assertion that N. ceranae affects honeybee health.


Input: Task: pass the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Received: 28 Apr 2022 – Discussion started: 02 Jun 2022 Publisher's note: remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors. Download & links Preprint (PDF, 2594 KB) Download & links Preprint (2594 KB) and Status: closed Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse RC1: 'Comment on tc-2022-92', #1, 08 Jul 2022 Introduction: The current study presents a statistical method that uses SIC and SIT from a reanalysis dataset to construct a historical SIT dataset. The idea behind this is that the SIT of the source reanalysis dataset is not accurate in the melt season as no SIT measurements in the melt season are available to feed into that reanalysis dataset, and that incorporating statistical relationships between SIC and SIT leads to an improved SIT dataset. Detailed comparisons with in situ observations and other often-used SIT datasets show that the newly developed SIT performs well. In addition, assimilation runs are performed in which only SIC or both SIC and the newly constructed SIT dataset are assimilated, which are then used to initialize 7-day forecasts. The skill of forecasts initialized from the assimilation runs in which both SIC and the new SIT dataset are assimilated is shown to be higher than forecasts initialized from assimilation runs in which only SIC was assimilated. The analysis shown is detailed and interesting, but there are several major issues that the authors have to address before I can recommend publication. Major comments: understand that the authors are not native English speakers, the English is poor, which makes it difficult to follow the text. I suggest the authors improve the language by consulting with an English native speaker. Perhaps it is due to my lack of expertise in the area, or the poor English (or a combination of these factors), but I do not fully understand the statistical model that is used to construct the SIT as described in section 3. 1 and in figure 2. Other readers may have similar problems and therefore this should be improved. In particular, I do not understand the ‘linear regression for each grid point’. What is particularly confusing is that the authors write that (l. [SEP] Justify:

Output: The text is largely focused on discussing the methodology of a study regarding sea ice thickness (SIT) and does not address the claim about the recovery of Arctic ice or the long-term effects of previous melting. It contains comments about statistical methods and concerns about language clarity but does not provide relevant information to support or contrast the claim.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: K. Lafferty in Whale, “Eschrichtius robustus,” M. L. Jones S. L. Swartz S. Leatherwood eds. , Academic Press, Orlando, FL (1984), p. 375. 3. R. S. Payne and D. Webb Ann. NY Acad. Sci. 188, 110 (1971). 4. B. M. Culik et al, Mar. Ecol. : Prog. Ser. 211, 255 (2001). 5. B. Southall et al, Aquat. Mammals 33, 411 (2007). 6. C. J. Stone of on in UK Waters, 1998–2000, rep. no. 323, (2003), available at H. Whitehead Sperm Whales: in the Ocean, U. Chicago Press, Chicago (2003). 8. M. Johnson et al, Proc. R. Soc. London, Ser. B (suppl. ) 271, S383 (2004). 9. P. J. O. Miller et al, Deep-Sea Res. , 56, 1168 (2009). 10. A. Frantzis Nature 392, 29 (1998). 11. D. L. Evans G. R. of 15–16 March 2000, US Department of Commerce and the Secretary of the Navy, Washington, DC (2001), available at for example, T. M. Cox et al, J. Cetacean Res. Manage. 7, 177 (2006). 13. N. A. Soto et al, Mar. Mammal Sci. 22, 690 (2006). [SEP] Justify:

Output: The text lists references and publications but does not address the claim regarding whether all marine mammals communicate in complex human-like languages. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Nosema ceranae is a parasite that affects honeybee health. [SEP] Text: Infection and sample collectionN. ceranae spores were isolated from the mid-guts of one hundred heavily-infected honey bee workers from two colonies in the apiary. The abdomens of honey bee workers were homogenized using pestles in 500 mL distilled water, filtered through filtering mesh cloth (65 um pore size) and centrifuged 5 min at 3000 rpm. Spores were further purified using a Percoll gradient procedure [19]. Sealed brood frames were collected from multiple colonies and the brood frames were kept in an incubator to collect freshly emerged honey bee workers. Eighty newly-emerged workers were fed individually with 2 μL of sucrose solution as follows: (1) with 105 N. ceranae spores without siRNA treatment, as the infection group; (2) with 105 N. ceranae spores and 1. 5 μg siRNA (targeting N. ceranae gene Dicer) as the siRNA-Dicer group; (3) with 105 N. ceranae spores and 1. 5 μg of a non-matching (scrambled) siRNA, as the siRNA-scramble group. The sequences of the designed siRNA were provided in a previous study [23]. Forty honey bees were kept in each rearing cup. In total, six rearing cups were constructed for three treatments, with two replicates per treatment. Five bees were sampled daily from 1 to 6 days post-infection (dpi) from each cup and ultimately pooled for RNA sequencing. After being anesthetized with CO2, the mid-gut tissue was dissected and the RNA was immediately extracted with TRizol [39]. Sequencing libraries were prepared and sequenced using the 2000 platform. In total, 36 PCR-free Illumina RNA paired-end (151 nucleotide per read) libraries of mid-gut samples were sequenced (two replicates per treatment per day), at the University of for Genomic Studies. Bioinformatics and microbial quantificationOn average, 58 million reads (114 nucleotides for each paired-read member) were generated from each library, after trimming and quality control using FastQC ( and SEQTK ( Sequencing reads were aligned to the honey bee genome assembly (Amel_HAv3. 1) with the hisat2 package under default settings [40]. [SEP] Justify:

Output: The text supports the claim by providing detailed information about the isolation of N. ceranae spores from heavily-infected honey bee workers. The methods described for infection, including feeding bees with N. ceranae spores and subsequent analysis, imply a direct connection between the parasite and the health of honeybees as they are the subjects of infection and evaluation in the study.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: The “retrospective forecast experiments” in Section 5 are really only cursory, with less than one month of forecasts performed for only one particular year. To properly assess the impact of assimilating the a much more comprehensive assessment would be needed – including the whole summer period on at least 2 different years. Many would also question the fact that BRMT uses information from the future (i. e. , 2012-2018) in the reconstruction. This makes it unusable for real-world forecasting situations. How much skill would be lost if you were to only use past data? I struggle with the concept and motivation for the reconstructed SIT dataset. BRMT essentially uses the relationship between SIC and SIT in the TOPAZ reanalysis. Much of the motivation for using TOPAZ in this way is not included and so I am left with so many questions in my head: So why not just use TOPAZ? What extra is BRMT bringing to the table? Why do you trust the relationships in TOPAZ so much? How much difference would using a different reanalysis make to the SIT reconstruction? I’m also concerned that there is some horrible kind of circularity in the analysis here, whereby desired traits – such as the relationship between SIC & SIT – are included in the design of the system and then used as part of the evaluation. The comparison of the model-based and reconstructed SIT observations with the in-situ observations is either not performed carefully enough or not described adequately. I am not convinced that these comparisons are being performed or interpreted correctly for the following reasons: The authors do not specify anywhere how they define Thickness (SIT). Is it the "floe thickness" (i. e. , the average thickness of all the sea ice floes present in the grid-cell) or the "grid-box-mean thickness" (i. e. , sea ice volume per unit grid-cell area)? The former is certainly what your point/in-situ observations (BGEP/IMB) are measuring. However, the latter is the prognostic used in the sea ice continuum models formulation and so very likely what you are using from the model-based products (TOPAZ///etc. ). Obviously for SIC=1 these definitions are the same but not for SIC<1. [SEP] Justify:

Output: The text contrasts the claim by expressing doubts regarding the reliability of the data and methodologies used to forecast ice conditions. It highlights concerns about the limited scope of the analysis ('less than one month of forecasts performed for only one particular year') and questions the validity of using future information in assessments ('BRMT uses information from the future'). This suggests that there is uncertainty and potentially significant issues in understanding the current state or recovery of Arctic ice, directly contradicting the claim that the majority of ice has recovered and that there are no long-term impacts.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: marine mammal species have been observed along the Syrian coast may be for feeding or breeding, or migration behavior, but the most realistic reason, from our point of view, is to increase scientific monitoring along the Syrian coast, as well as to raise awareness of fishermen about the importance of reporting their sightings to the research team at the for the Protection of the and at Tishreen University, Which allowed the recorded of several stranding every year in different areas on the Syrian coast [8, 9, 10, 11, 12], as summarized them in Table 1, bearing in mind that there may be a few stranding that was not monitored, perhaps because of the remoteness of the area or the lack of reporting. Hence, our objective in this work is to compile and review the records and strandings of marine mammals along the Syrian coast and to provide further suggestions to protect these vulnerable species. Advertisement 4. Results of a field survey onboard research ships in August 2008In partnership with a team from the Faculty of Fisheries—Istanbul University, within the framework of a joint Syrian–Turkish–Lebanese research program. To determine the species composition, size, and distribution of the cetacean population, sighting data were collected during a research cruise carried out in summer (July 11–24, 2008) in the international water of the Sea, as well as the Turkish, Lebanese and Syrian territorial waters. This research cruise was conducted with a 32-m research vessel YUNUS—from the faculty of fisheries—at Istanbul University. The average speed of the vessel was 8–10 nautical miles/h. At each cetacean sighting, date and time, species, the group size of animals, location (coordinates), depth, sea state, and the behavior of animals were recorded by a single observer placed on the bridge deck. Totally 860 nautical miles of survey effort were made and 16 sightings (108 animals) were recorded. During the study, 5 Physeter macrocephalus in one sighting, 72 Stenella coeruleoalba in five sightings, 2 Grampus griseus in one sighting (associated with S. coeruleoalba individuals), and 2 Delphinus delphis in one sighting, and 27 Tursiops truncatus in nine sightings were recorded. The overall encounter rate was 0. 18 sightings/10 nm [13]. Advertisement 5. [SEP] Justify:

Output: The text does not address the claim about marine mammals communicating in complex human-like languages. Instead, it focuses on research conducted to monitor and report sightings of marine mammals along the Syrian coast, detailing the scientific methods and observations made during the study. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Nosema ceranae is a parasite that affects honeybee health. [SEP] Text: Reads that matched the honey bee genome were removed. On average, 14,258,564 remaining reads per library were then aligned to the Holobee ‘Bar’ database and 7,260,989 reads per library were mapped (>138X coverage of the database) including N. ceranae and other bee-associated microbes. Bar database is a curated resource for microbes associated with honey bees ( In order to remove false-positive microbial classifications and restrict reads to the more prevalent bacteria responding towards the parasite infection, microbes found in fewer than 22 libraries of the sequencing libraries were removed. The counts were normalized using the weighted trimmed mean of M-values (TMM). Two replicates of each treatment for each post-infection day were used to calculate the variance in order to identity significantly regulated bacteria with edgeR [41]. values were corrected for multiple comparisons using false discovery rate (FDR). Significantly differentially expressed taxa met three criteria: (1) the relative abundance of the bacterium is significantly different between the infection group and the siRNA-Dicer group; (2) the relative abundance of the bacterium is significantly different between the siRNA-Dicer group and the siRNA-scramble group; and (3) the relative abundance of the bacterium is not significantly different between the siRNA-scramble group and the infection group. Statistical analysisSignificantly differentially expressed taxa were tested using t-tests at each time point and the P value was adjusted for multiple comparisons with FDR using R (version 3. 5. 1). In order to analyze bacterial patterns over the infection period, co-regulation between N. ceranae and bacteria clusters was analyzed using the WGCNA package [42]. The normalized read counts of bacteria were treated as the genotype and counts of N. ceranae were treated as phenotype. To further test the effect of siRNA treatment on the microbe enrichment, a generalized linear model was performed using R (version 3. 5. 1). Within the linear model (counts ~ treatment + day + cage + group + microbe), counts indicate normalized microbe counts. Treatment indicates siRNA-Dicer group, siRNA-scramble group and infection group. Day indicates number of days post infection. Cage indicates each of the two cage replicates. Group indicates co-expression groups obtained from WGCNA analysis. [SEP] Justify:

Output: The text corroborates the claim by discussing the analysis of microbial data, specifically mentioning 'N. ceranae' as being included among the mapped reads to the Holobee database. The context indicates that N. ceranae is being researched in relation to honeybee health and infection dynamics, suggesting that it indeed affects honeybee health.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: Another consideration is that if using the "grid-box-mean thickness" definition, SIT will likely be much more correlated with SIC than using the "flow thickness" definition. There is no discussion of how much one would expect agreement between the model and the in-situ observations. In particular the BGEP data are point observations (of ice draft converted to thickness) and are being compared with the modelled thickness in a large grid-cell. Even for the case SIC=1, when the SIT definition issue above is not present, a direct comparison is not obvious. It might be that spatial and temporal averaging of the BGEP data makes it comparable to the mean of the model ITD but that is not discussed. The same is true for the IMBs which will only ever model a single floe. Furthermore there is no discussion of the sampling issues one would expect in the IMB dataset. The IMBs are of course Lagrangian in nature and are permanently attached to the same ice floe. So one would expect changes in thickness to relatively slow, given that they are purely driven by thermodynamics. Meanwhile the dynamical nature of the Eulerian model could have huge changes in thickness from one time-step to the next. Finally, there are known sampling biases in the IMB dataset that should be discussed. IMBs are normally deployed just before the freeze-up in ice that has survived the summer. Mid-thickness floes are normally chosen – avoiding thick floes for practical reasons and thin floes to limit the chance of losing expensive equipment too quickly. Minor comments/concerns In many cases results seem somewhat overstated. In particular the performance of BRMT in the region, described as ‘outstanding’, is based upon comparison with only 2 IMB buoys – although the Lagrangian trajectory will include several individual measurements, they will only be of 2 individual ice floes! (NB. the same is true for the forecast results, which are based on only a few forecasts performed in a single year, but this is already mentioned above. ) Too little information is provided about the model/reanalysis products being used. In particular, what observations are assimilated and what surface forcing is being for the reanalyses. This applies for both the reanalyses datasets in Section 2 and the MITgcm model in Section 3. [SEP] Justify:

Output: The text contrasts the claim by discussing various sampling issues and methodological concerns associated with measuring ice thickness and distribution. It highlights the potential biases and limitations of observational data that could impact conclusions regarding the recovery of Arctic ice. The mention of sampling biases and the nature of IMB datasets suggests that there may be long-term implications of previous ice melting that are not being fully accounted for, thereby undermining the assertion that the majority of ice has recovered and that no long-term effects exist.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: Results of a field survey on abundance and distribution of cetaceans in the Syrian waters of the the framework of participating in the work of the ASI1 project launched at the of the Parties in 2 (Monaco, 22–25 November 2016), with the aim of creating an integrated, collaborative, and coordinated monitoring system of the state of cetaceans within the region (Black Sea, Mediterranean Sea, and Area), which was developed and implemented by the permanent secretariat of in coordination and with the support of the riparian countries of the Mediterranean and local scientists, the author led a team of local experts. Researchers and trainees conducted visual surveys in Syrian territorial waters [14], where surveys were conducted onboard a ship chartered by the ASI Project called Okeanos, on two sets of perpendicular tracks on the coast (Figure 3) during the period: July 27 until August 8, and included 431 km of effort on track. The tracks cut a few miles from the Lebanese and Turkish borders. During the survey, five cetacean species were documented by sightings (Table 2) (Figure 4) [14, 15]. Figure 3. Scheme of the field survey of marine mammals in the Syrian territorial waters (the tracks of the Okeanos boat along the Syrian coast) which was implemented within the ASI–project during the period from July 27 to August 8, 2019. Figure 4. Pictures of five dolphins (T. truncatus) in front of the beach of Wadi Qandil, about 30 km north of Latakia, during the field survey process by direct viewing onboard the boat Okeanos within the framework of the ASI–project: 2 August 2019 [14]. Figure 5. A female Mediterranean monk seal was found carrying a full fetus that had mistakenly been killed (Port of Latakia, Syrian coast, 7/22/2013). Figure 6. An individual monk seal was seen swimming near the beach of Burj Islam, 20 km north of Latakia, 12 December 2021. Advertisement 6. Monk sealmonk seal (Monachus monachus) is a common mammal in the Syrian coastal water, especially in the northern parts of the Syrian coast near the Syrian–Turkish border. These animals live up to 45 years and their length is approximately 2. 5 m. Male and female Mediterranean monk seals can be easily distinguished since the first ones have black color, while the second ones have brown fur. [SEP] Justify:

Output: The text provides detailed information on a field survey of marine mammals, including species documentation and the methodology used in the survey, but it does not discuss any aspects of communication within marine mammals. It is irrelevant to the claim regarding the complexity of marine mammal communication, as it focuses solely on the abundance and distribution of cetaceans without addressing their language or communication abilities.


Input: Task: corroborate the claim using the given text. Claim: Nosema ceranae is a parasite that affects honeybee health. [SEP] Text: of Nosema ceranae to different exposure conditions has been evaluated by using Sytox green and DAPI (4′,6-diamidino-2-phenylindole) to test spore viability. High thermotolerance at 60 and 35°C and resistance to desiccation were observed. However, a significant decrease in viability after freezing and a rapid degeneration of spores maintained at 4°C were also detected. species have been related to pathology in the honeybee: Nosema apis (18) a parasite of Apis mellifera, the western honeybee, and Nosema ceranae (4), a parasite of Apis cerana, the eastern honeybee. Currently, however, N. ceranae is considered an emergent and important parasite of Apis mellifera (4). Over the last few years, an increase in infections by this microsporidian has been detected in several European countries, together with an increase in honeybee colony deaths and a consequent decrease in the production of honey (9). However, it is not clear if N. ceranae infection may be the only factor related to this disorder, since this pathogen has also been found in healthy colonies (14). In Spain, Higes et al. (7, 9) have demonstrated the presence of this parasite in honeybee samples from colonies with clear signs of population depletion, relating the colony collapse disorder to N. ceranae. The presence of this microsporidian is not exclusive to Europe, since it has also been described in bee samples collected about a decade ago in the (3). The pathology produced by N. ceranae in A. mellifera bees may be higher than that produced by N. apis, showing a rapid autoinfective capacity of the spores to spread the infection among epithelial cells, producing high mortality (6). On the other hand, reduced longevity of caged N. ceranae-infected worker bees compared to bees infected by N. apis has also been found (15). To date, continuous cultures of N. ceranae are not available and there is no effective treatment. For this reason, it is important to study the effects of different exposure conditions, such as time, temperature, and desiccation, on the viability of spores kept in the laboratory for use in the search for new treatments and for development of culture protocols. [SEP] Justify:

Output: The text corroborates the claim by stating that 'N. ceranae is considered an emergent and important parasite of Apis mellifera,' indicating its impact on honeybee health. Additionally, it discusses the association between N. ceranae and colony collapse disorder as well as increased mortality among infected bees, further supporting its relevance as a parasite affecting honeybee health.


Input: Task: contrast the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: RMSE of SIC is not a very good metric for sea ice forecasts because of the errors in the passive microwave satellite observations. This is particularly true in the summer when the SSMIS cannot distinguish surface melting/ponds from open water. accuracy is also lower in areas of low concentration or thin ice. These points are the motivation for people using sea ice “extent” to compare with satellites. In particular, I would drop the MIZ analysis in 5. 2. 1/Figure 15 because the SSMIS satellite is likely not able to resolve that. If you redid this analysis using AMSRE2 observations (which is higher resolution and more able to resolve thin/low concentration ice) then the results could be quite different. Some of the figures do not bring any useful information and so could either be removed or reformulated. For example, the data in Fig 13 can be understood easily from Fig 12b. Similarly, Fig 14, for which all the panels in look the same, could be improved by changing the model fields on the lower rows to be model-obs differences. Typos and technical comments I attach and annotated version of the original pdf with technical comments. I do not highlight all instances where the English language needs to be improved only the cases where the language is unclear to the point that the scientific understanding is inhibited. Citation: AC2: 'Reply on RC2', Lu Yang, 28 Aug 2022 We thank reviewer RC2 for their constructive review. We have replied to comments from all commenters and reviewers in the attached document. Citation: Report abuse × Please provide a reason why you see this comment as being abusive. You might include your name and email but you can also stay anonymous. Please provide a reason why you see this comment as being abusive. Please confirm reCaptcha. Comment* Name: Email: Share Status: closed Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse RC1: 'Comment on tc-2022-92', #1, 08 Jul 2022 Introduction: The current study presents a statistical method that uses SIC and SIT from a reanalysis dataset to construct a historical SIT dataset. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of sea ice concentration metrics and the potential inaccuracies in satellite observations. It mentions that the RMSE of SIC is not a good metric for sea ice forecasts, especially in summer when distinguishing between melting and open water is difficult. This suggests that understanding the state of Arctic ice may be more complex than just stating that it has recovered, thereby challenging the claim that there are no long-term effects from previous melting.


Input: Task: pass the claim using the given text. Claim: All marine mammals communicate in complex human-like languages. [SEP] Text: After being considered “critically endangered” for the previous 19 years, the Mediterranean monk seal Monachus monachus (Hermann, 1779) has been classified as “Endangered,” according to the. Although the population is doing better in the Mediterranean, the monk seal remains a relatively rare species and difficult to observe with large consumption of fish per day, the animal has long been the bane of fishermen in the archipelago because they regularly tore their nets. Gradually, he succeeded in escaping their radars and by the same to those of the scientists who deplore the insufficiency of information. Hunted for a long time, we are now trying to get to know the animal better. Hope today lies in the development of new tools for the preservation of marine biodiversity. These species are threatened by human activity and environmental pollution. More than 32 sightings of this animal have been recorded in the waters of the Syrian coast between 1996 and 2021. 22, 2013, a female Mediterranean monk seal was found carrying a full fetus (Figure 5), who had mistakenly killed someone who had infiltrated the main port with the aim of sabotaging. The victim’s mother and her chick were placed in two fiberglass basins filled with formalin. 12, 2021, the presence of two seals was documented in a cave on the seashore in the Samra region on the Syrian–Turkish border, and one individual was seen swimming near the beach of Burj Islam, 20 km north of Latakia, where there are several caves believed that seals take refuge in them to rest or take care of their young (Figure 6). It is worth noting that the author recorded sounds of monk seals in a rocky cave south of on July 16, 2020, and these sounds indicate the presence of more than one individual. A seal swimming in the same place was previously documented in 2005 [16]. In addition, according to local residents and fishermen, there are eight seals that were seen intermittently roaming the waters between Burj Islam, (20 km north of Latakia), and Samra, on the Turkish–Syrian border. Before the end of this 3. Comparison between the marine mammal species that were previously documented nearly a century ago and those whose existence has been documented in Syrian waters during the last two decades. Advertisement 7. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the Mediterranean monk seal’s conservation status, population sightings, and interactions with fishermen without providing any evidence or information about the communication methods of marine mammals. It fails to support or contradict the idea that all marine mammals communicate in complex human-like languages.


Input: Task: corroborate the claim using the given text. Claim: Nosema ceranae is a parasite that affects honeybee health. [SEP] Text: In addition, as different levels of thermotolerance in the environment and different epidemiological patterns have been described for these microsporidia, available data on resistance of N. apis spores cannot be extrapolated to N. ceranae. Spores. Spores from N. apis and N. ceranae were provided by the experimental apiary of the Center in Marchamalo, Spain. Recently collected spores were purified as described previously (8). The spore concentration was determined by counting with a hematocytometer chamber. Two stocks of N. ceranae spores were used (S1 and S2). All studies were carried out at a concentration of 5 × 105 spores/ml. species was determined by multiplex PCR, which amplifies the 16S rRNA locus (12). Spore treatments. The effects of time, temperature, and desiccation on viability were measured at different points. All studies were carried out in duplicate. The effect of temperature on N. ceranae spores (S1) maintained in 1× phosphate-buffered saline (PBS) was measured after treatment for 1, 2, 4, and 6 h at 60°C or 2 h at 35°C or after having been autoclaved. Viability was again tested on the same batch of spores after treatment 1 month later, maintaining the spores at 4°C in 1× PBS. To study the effect of freezing on spores of N. ceranae, they were maintained at −20°C with RPMI medium plus 10% dimethyl sulfoxide (DMSO) or with fetal bovine serum (FBS) plus 10% DMSO or were air dried with no media for 1 week or 3 months. The effect of desiccation was determined with air-dried spores maintained at room temperature (RT). The viability was measured at 12 h, 48 h, and 1 week later. The viability of N. ceranae spores (S2) and N. apis, maintained in 1× PBS at 4°C for 12 months, was also studied. In each group, a negative control consisting of spores kept at 4°C was used to compare the evolution of viability. Staining procedure. A modification of the method of Green et al. (5) was used. [SEP] Justify:

Output: The text discusses the studies related to N. ceranae, including the effects of environmental variables on its spore viability. By mentioning the purification and viability testing of N. ceranae spores, it implies that N. ceranae is being researched as a significant factor, supporting the claim that it is a parasite impacting honeybee health.


Input: Task: pass the claim using the given text. Claim: The majority of ice in the Arctic has already recovered, and there are no long-term effects from previous melting. [SEP] Text: However, there are some major issues that will need to be addressed before the manuscript can be published. Major comments/concerns The quality of English language used throughout the manuscript leaves a lot to be desired. As well as reducing the overall readability of the paper, there are also several cases in which it is hard to understand what is being said (or why). One of the main motivations listed for this study, and the BRMT dataset, is that summer satellite SIT data is not available. This issue is mentioned several times and the authors go so far as to state that it is “impossible”. However, the authors do not take into account the fact that summer satellite SIT has been being developed now for many years. The first dataset of summer Arctic radar freeboard (10-years) was published at the beginning of this year (Dawson et al. , 2022). (NB. conversion from radar freeboard to SIT has also been done but that paper is still in press. ) I’m not necessarily saying that the existence of these new datasets invalidates the motivation for this study, but it should definitely be referenced and included in the discussion. How does/would the BRMT compare with the Dawson et al. (2022) freeboard? The paper manages to be both too long and detailed, and too short and vague at the same time. By this I mean that, despite the paper being rather long, sufficient details are not provided of the methods used to create BRMT. Given the length of the paper, I recommend that the authors consider dropping Section 5 and focussing properly on the BRMT reconstructed dataset – both creation and evaluation. The “retrospective forecast experiments” in Section 5 are really only cursory, with less than one month of forecasts performed for only one particular year. To properly assess the impact of assimilating the a much more comprehensive assessment would be needed – including the whole summer period on at least 2 different years. Many would also question the fact that BRMT uses information from the future (i. e. , 2012-2018) in the reconstruction. This makes it unusable for real-world forecasting situations. How much skill would be lost if you were to only use past data? I struggle with the concept and motivation for the reconstructed SIT dataset. BRMT essentially uses the relationship between SIC and SIT in the TOPAZ reanalysis. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not address the status of Arctic ice recovery or the long-term effects of melting. It primarily discusses concerns related to a manuscript's quality and dataset issues, specifically regarding the BRMT dataset and summer satellite SIT data. Therefore, it does not provide any information to corroborate or contrast the claim.