Input: Task: corroborate the claim using the given text. Claim: Entomopathogenic nematodes can target soil-dwelling pests. [SEP] Text: 7:314-318Poinar, G. O. , Karunakar, G. and David, H. (1992). Heterorhabditis indicus n. sp. (Rhabditida:Nematoda) from India: separation of Heterorhabditis spp. by infective juveniles. Fundam. Appl. Nematol. 15: 467 -472. Prabhuraj, A. , Viraktamath, C. A. , Kumar, A. R. V. ( 2000). Field evaluation of entomopathogenic nematodes against brinjal root weevil Myllocerus discolors Boh. 6:149-151. Prabhuraj, A. , Viraktamath, C. A. , Kumar, A. R. V. (2003). Pathogenicity of two entomopathogenic nematodes against Holotrichia serrata (Fabricius) (Coleoptera: Scarabaeidae) and Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae). In: Biological control of lepidopteran pests. Proceedings of the Symposium of of Lepidopteran Pests. (eds) 17-18,2002, Bangalore, India,209. Rai, A. B. , Halder, J. and Kodandaram, M. H. (2014). Emerging insect pest problems in vegetable crops and their management in India- An appraisal. 20:113-122Rashid, P. (2010). Biocontrol potential of entomopathogenic nematodes against gram pod borer, Helicoverpa armigera (Hubner). Curr. Nematol 21:17-21. Ratansinghe, G. and Hague, N. G. M. (1998). The invasion, development and reproduction of Steinernema carpocapsae (Rhabditida: Steinernematidae) in the diamondback moth, Plutella xylostella (Lepidoptera: Yponomeutidae). Nematropica 28: 1-6. Salmanov, A. (1988). Results of attempted infection of the Colorado beetle with the nematode Neoaplectana carpocapsae in laboratory and field conditions. [SEP] Justify:

Output: The text corroborates the claim by providing multiple references to studies where entomopathogenic nematodes, such as Steinernema carpocapsae and Heterorhabditis indicus, are evaluated for their effectiveness against various pests, including specific soil-dwelling pests like the brinjal root weevil and the gram pod borer. These studies demonstrate the nematodes' ability to target and control soil-dwelling pest populations.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Supercategories: All categories, Arts & Entertainment, Autos & Vehicles, Beauty & Fitness, Books & Literature, Business & Industrial, Computer & Electronics, Finance, Food & Drink, Games, Health, Hobbies & Leisure, Home & Garden, Internet & Telecom, Jobs & Education, Law & Government, News, People & Society, Pets &. A special case was the party “Die Linke,” which nominated eight top candidates in 2013. By comparing the search queries of the individual top candidates separately, we filtered out the only two top candidates that seemed relevant for the prediction, who were “Gregor Gysi” and “Sarah Wagenknecht. ” For the AfD we used the search term “Afd” finding that it to be equivalent to “AFD” and “AfD”. Regarding the number of terms within a search query for a party, one could argue that the result of a search query should be divided by the number of search terms, because the search queries of some parties contain more individual terms and could thus be overrepresented (e. g. if a party has a dual leadership). In our opinion, this argumentation is not plausible, because the search terms for one party never provide an equal share of the search interest. The share of the term for the party abbreviation is much higher than that for the party candidates, so it is illogical to treat them equally. We checked this scenario by searching only for the more popular candidate when there is a dual leadership, which results in the same amount of search terms for each party. This, however, leads to much worse predictions than our main analysis. Thus, we assume that the parties are not overrepresented by their multiple top candidates. Choosing a period longer than 270 days for the election our years, results in getting weekly data instead of daily data. One might also ask if it would make a difference to pull one dataset that covers all interval and distance combinations, as in our case, or to pull single datasets for each interval and distance combination. We checked this and it made no difference since we only use the comparison function of Google Trends. Accordingly, the ratio of the parties to each other remains the same [29]. The term model does not necessarily refer to a sophisticated statistical model here. For instance, the models based on polls are simply the party shares as measured in the polls. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and nuances involved in predicting outcomes based on data analysis. It mentions that different methodologies, such as using search query data for political party predictions, can lead to varying results and that simplifying complex data into a model is not straightforward. For instance, it states that treating search queries equally does not yield accurate predictions, highlighting that the prediction process is complex and not foolproof, thereby contradicting the idea of perfect accuracy.


Input: Task: pass the claim using the given text. Claim: Every culture's history is accurately recorded in their artifacts. [SEP] Text: Recorded clinic conversations were evaluated instead of live clinic conversations to minimize interruption to patient care in this initial study because the overall accuracy of these devices is unknown. Success in this study would encourage further study of these devices in live clinic settings and comparison to the gold standard of in-person medical interpreters. Ultimately, successful implementation of this device has the potential to benefit interactions between clinicians and Spanish patients with implications for other languages as well, particularly in areas of clinic workflow where minimal elaboration is required such as intake and triage or where access to translators is limited. Materials and Participants included English and Spanish-speaking patients and their families who visited an academic health instiution’s outpatient pediatric neurosurgery clinic between January 2022 and March 2023. This was a sample of convenience for participation in the study and consent was obtained from caregivers, patients, and the physician. Ethics approval was reviewed and approved by the University of at Board (192105). Recording of visits The study consisted of audio recording outpatient clinical encounters with a physician wearing a bluetooth microphone transmitting audio to a secure clinic iPad. All participants were informed during the consent process that they had the option of stopping the audio recording at any point during the visit. Following the visit, the audio was uploaded to a secure REDCap electronic database. Translation & translation device consisted of a smartphone with live translation function. Recordings were later played back by investigators and the team’s certified medical translator to the translation device in a secure location. If the conversation took place in English, the device translated to Spanish, while if the conversation took place in Spanish, the device translated it to English. Five minute segments from each recording were randomly selected for analysis, and the following metrics were analyzed: transcript length, incidence of minor errors, errors impacting understanding, and errors affecting understanding of medical diagnosis or care. Population data from the visits and summary data for each outcome was computed. A 2-sample t-test compared transcript length and a 2-proportion t-test compared incidence of minor errors, errors impacting understanding, and errors affecting understanding of medical diagnosis or care between the English to Spanish and Spanish to English translation groups. All statistics and analysis were performed in GraphPrism 10. ResultsA total of 50 patient visits were recorded. 40 of the visits involved primarily English speaking recorded conversations that were translated to Spanish by the device, while the remaining 10 were primarily Spanish speaking recorded conversations that were translated to English by the device. [SEP] Justify:

Output: The text discusses a study about evaluating recorded clinic conversations and translating them, which does not relate to the claim about the accuracy of cultural histories recorded in artifacts. The content focuses on medical interactions and the use of translation devices rather than cultural or historical artifacts.


Input: Task: corroborate the claim using the given text. Claim: Entomopathogenic nematodes can target soil-dwelling pests. [SEP] Text: Izvestiy a 2: 45-47Saravanapriya, B. and Subramanian, S. (2007). Pathogenicity of entomopathogrnic nematodes to certain foliar insect pests. 15: 219-222. Schalk, J. M. and Creighton, C. S. ( 1989). Influence of sweet potato cultivars in combination with a biological control agent (Nematoda: Heterorhabditis heliothidis) on larval development of the banded cucumber beetle (Coleoptera: Chrysomelidae). 18: 897-899. and Petzoldt, C. ( 1996). Greenhouse and field evaluations of entomopathogenic nematodes (Nematoda: Heterorhabditidae and Steinernematidae) for control of cabbage maggot (Diptera: Anthomyiidae) on cabbage. J 89: 1109-1115. Schroer, S. and Ehlers, R. U. (2005). Foliar application of the entomopathogenic nematode Steinernema carpocasae for biological control of diamondback moth larvae (Plutella xylostella). 33: 81-86. Sher, R. B. , Parrella, M. P. and Kaya, H. K. (2000). Biological control of the leaf minor Liriomyza trifolii (Burgess): implications for intraguild predation between Diglyphus begini Ashmead and Steinernema carpocapsae (Weiser). 17: 155-163. Shilpa, S. , Singh, N. P. and Shinde, S. (2000). Susceptibilty of diamondback moth Plutella xylostella (L. ) to entomopathogenic nematodes. 38: 956-959. . and Tripathi, C. P. M. ( 2015). Diversity analysis of entomopathogenic nematodes against Helicoverpa armigera (Hubner) from Tarai region of IGP, India. Sci 1:15-23. Stepanova, N. E. and Perera, M. ( 1993). Insect pathogenic nematodes in the control of the potato moth. Moskva 5:20. Stewart, J. [SEP] Justify:

Output: The text corroborates the claim by discussing multiple studies that evaluate the pathogenicity of entomopathogenic nematodes against various insect pests, including references to their applications both in field and greenhouse settings. For example, it mentions the control of cabbage maggot and diamondback moth larvae, indicating that these nematodes can indeed target and affect certain pests.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: A special case is MC2 for the 2013 election, for which the 2009 election is used to calculate the weighting factor. The party AfD was founded in 2013, as a consequence, we do not have data from 2009. We circumvented this problem by not weighting the AfD’s Google proportion in 2013, which should be taken into account when looking at the results. Occasionally in 1 or 3 week intervals. In the actual data the polls do not appear exactly every two weeks. If there is no poll in our data window, we use the first poll before the data window. In the case where the start time of the data window and a poll fall on the same day, the Google proportion up to the 1st poll before the data window is used and divided by the poll falling on the same day as the start time to calculate the weighting factor. Subsequently, the Google proportion starting one day after the start date and poll up to the first poll in the interval is weighted. Polls that fall on the same day as the end day of an interval are ignored because it wouldn’t make sense to calculate a weighting factor using only one day of Google data and applying it on the same day. Obtained by averaging predictions over 10 GT data samples. The graph is only showing a subset of our predictions restricted to one election and four data window widths. Overall, from our models that are solely based on GT data we yielded 27,600 predictions, namely predictions for 6 parties of based on 4800 models defined by 8 widths, 150 distances and 4. For the 2009 election, we have 21,600 solely GT data based predictions. For the 2013, 2017, 2021 elections we obtained 21,600 predictions. 9. 4. 80. 62. 91. 9. 2022. Desktop and mobile search market share of search engines in Germany in December 2023 [Graph]. Statista. 2023. Ten years of research change using Google trends: from the perspective of big data utilizations and applications. Soc Change. 2018;130:69–87. 7258. p. 213–8. JT. Trending in the right direction: using Google trends data as a measure of public opinion during a presidential election. Blacksburg: Virginia Tech; 2018. 2015. N. Predicting the Irish’Gay Marriage’Referendum. SSRN 2674243. 2015. [SEP] Justify:

Output: The text contrasts the claim by illustrating the limitations and complexities of using AI to predict outcomes based on past election data. It describes the challenges in data collection and the specific methodologies used to circumvent gaps in the data, such as not weighting the AfD's Google proportion in 2013 due to a lack of pre-existing data. This indicates that while AI can generate predictions using data trends, achieving 'perfect accuracy' is not feasible due to the reliance on historical data that may not be complete or perfectly reflect future scenarios.


Input: Task: pass the claim using the given text. Claim: Every culture's history is accurately recorded in their artifacts. [SEP] Text: 23 (46%) of the pediatric patients were female, and the racial identification of the pediatric patients was 34 (68%) White, 6 (12%) Black and/or African American, and 10 (20%) other races. With respect to ethnicity, 10 (20%) identified as Hispanic. The mean age of the pediatric patients was 11 years. (Table 1) Table 1 Demographic information n (% all) Patient Encounters, n 50 Female sex 23 (46%) White 34 (68%) Black/6 (12%) 10 (20%) 10 (20%) English as primary language 40 (80%) Spanish as primary language 10 (20%) Mean age ± SD (years) 11 ± 5 The total mean transcript length was 4244 ± 992. The overall translation accuracy was 98. 2% ± 0. 5% On average, 46 words were missed in translation (1. 09% error rate), 29 understanding-altering translation errors were made (0. 68% error rate), and 2 errors impacting diagnosis or care (0. 05% error rate) were made. (Table 2) Table 2 Translation results Mean characteristic n ± SD (% of total) All participants (n = 50) (n = 40) (n = 10) T score difference between English and Spanish % P value Total words 4244 ± 992 (100%) 4186 ± 778 (100%) 4476 ± 615 (100%) t = 0. 18 p = 0. 85 Missed words 46 ± 20 (1. 09% ± 0. 46%) 45 ± 13 (1. 08% ± 0. 31%) 49 ± 15 (1. 09% ± 0. 33%) t = 0. 14 p = 0. 88 Understanding- altering error 29 ± 15 (0. 68% ± 0. 35%) 27 ± 10 (0. 64% ± 0. 24%) 36 ± 12 (0. 80% ± 0. 27%) t = 0. 42 p = 0. 67 Medically impactful error 2 ± 1 (0. 05% ± 0. 02%) 2 ± 1 (0. 05% ± 0. 02%) 3 ± 1 (0. 07% ± 0. 02%) t = 0. 48 p = 0. 63 Accuracy 98. 2% ± 0. 5% 98. 2% ± 0. 3% 98. 2% ± 0. 4% t = 0 p = 0. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses demographic data from a pediatric study, focusing on translation accuracy and patient characteristics. It does not address or provide any information related to the recording of cultural histories in artifacts.


Input: Task: corroborate the claim using the given text. Claim: Entomopathogenic nematodes can target soil-dwelling pests. [SEP] Text: G. , Boiteau, G. and Kimpinski, J. (1998). Management of late season adults of the Colorado potato beetle (Coleoptera: Chrysomelidae) with entomopathogenic nematodes. 130: 509-514. Tacconi, R. , Boselli, M. , Curto, G. M. and Santi, R. (1999). Entomopathogenic nematodes in the control of the sugar beet weevil. 49: 20-22. Torres, B. A. , Suazo, A. , Buhler, W. G. , Cardoza, Y. J. ( 2011). Studies on the entomopathogenicity and bacterial associates of the nematode Oscheius carolinensis. 59:123–129. Trdan, S. , Vidrih, M. , Andjus, L. and Laznik, Z. (2009). Activity of four entomopathogenic nematode species against different developmental stages of Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera,Chrysomelidae). Helminthologia 46:14-20. Vashisth,S. ,Chandel,Y. S. and Sharma,P. K. (2013). Entomopathogenic Nematodes- A review. 34:163-175. Weiseh,J. (1955). Neoaplectana carpocapsae n. sp. (Anguillulata, Steinernematinae). novy cizopasnik housenek obalece jablecneho, Carpocapsa pomonella L. Vest. Cs. spol. zool. 19: 44-52. Welch, H. E. ( 1958). Test of a nematode and its associated bacterium for control of the Colorado potato beetle Leptinotarsa decemlineata (Say). of Ontorio 88:53-54. Williams, E. C. and Walters, K. F. A. ( 2000). Foliar application of entomopathogenic nematode Steinernema feltiae against leaf miners on vegetables. Tech 10: 61-70. Wu, H. J. and Chow, Y. S. (1989). [SEP] Justify:

Output: The text supports the claim by providing references and studies that detail the use of entomopathogenic nematodes in controlling various pests, including the Colorado potato beetle and the sugar beet weevil. This indicates that these nematodes are indeed employed to manage soil-dwelling pest populations.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Mavragani A, Tsagarakis KP. Predicting referendum results in the big data Era. J Big Data. 2019;6(1):1–20. 2013. p. 000069–73. Sjövill R. to Predict the General Election: trends help predict the Election? 2020. Hey Google! Zealand vote to legalise cannabis? trends data to predict the outcome of the 2020 cannabis referendum. Drug Policy. 2021;90: 103083. 9. 5. 253. F. Assessing the accuracy of Google trends for predicting presidential elections: The case of Chile 2006–2021. Data. 2022;7(11):143. &amp%3Bref_topic=6248052Help GT. results by category - Trends Help. 2023. &ref_topic=4365530ínez IZ, Sax C. Obtaining consistent time series from Google Trends. Econom Inquiry. 2022;60(2):694–705. 2021. Polit. 2022;45(7):1506–21. T, Müller. model: a dynamic forecast of the 2021 German federal election. PS Polit. 2022;55(1):85–90. 41. Download 1. Category selection among supercategories of Google Trends. Figure S2. Search terms for other parties. Figure S3. Sampling error variation for the 2021 election. Figure S4. Accuracy of GT predictions for different parties and party shares across datawindows (more intervals). Figure S5. Accuracy of GT predictions for different parties and party shares across datawindows (more intervals, elections). Figure S6. Accuracy of predictions across model classes for 2009 election. Figure S7. Accuracy of predictions across model classes for 2013 election. Figure S8. Accuracy of predictions across model classes for 2017 election. Figure S9. Comparison across elections. Figure S10. Benchmarking against other polls (Elections: 2009, 2013). Figure S11. Benchmarking against other polls (Elections: 2017, 2021). Figure S12. Figure 4 with ‘other parties. Figure S13. Figure 5 with ‘other parties’ (2021 election). Figure S14. Figure 5 with ‘other parties’ (2009 election). Figure S15. Figure 5with ‘other parties’ (2013 election). [SEP] Justify:

Output: The text contrasts the claim as it discusses various studies and their methodologies in predicting election results using data trends, indicating that while AI and data trends can suggest probabilities, the reference to 'accuracy of predictions' suggests that predictions may not be perfect. The text highlights the need for consistency and error analysis in predictions, implying that perfect accuracy is not achievable.


Input: Task: pass the claim using the given text. Claim: Every culture's history is accurately recorded in their artifacts. [SEP] Text: 99 Comparing primary English and Spanish translations, the mean English transcript length was 4186 ± 778 words while the mean Spanish transcript length was 4476 ± 615, for which two sample t testing showed no significant difference (t = 0. 18, p = 0. 85). With respect to missed words, the mean error rate was 1. 08% ± 0. 31% in English to Spanish and 1. 09% ± 0. 33% in Spanish to English, a difference which was not significant by two proportion t-testing (t = 0. 14, p = 0. 88). Comparing understanding-altering errors, the mean error rate was 0. 68% ± 0. 35% in English to Spanish and 0. 64% ± 0. 24% in Spanish to English, a difference which was not significant by two proportion t-testing (t = 0. 42, p = 0. 67). Comparing errors affecting understanding of medical diagnosis or care, the mean error rate was 0. 05% ± 0. 02% in English to Spanish and 0. 07% ± 0. 02% in Spanish to English, a difference which was not significant by two proportion t-testing (t = 0. 48, p = 0. 63). Lastly, comparing accuracy, the overall accuracy was 98. 2% ± 0. 3% in English to Spanish and 98. 2% ± 0. 4% in Spanish to English, a difference which was not significant by two proportion t-testing (t = 0. 14, p = 0. 92). Overall, none of the translation outcomes significantly differed in this study between primary English to Spanish and primary Spanish to English translation. (Table 2)DiscussionMachine translation has undergone significant scrutiny, especially in sensitive domains such as clinical care where errors can impact patient decision making and understanding. Clinicians have been apprehensive about the integration of these devices into care due to a lack of research into their efficacy. 10,11 Previously noted critical errors and inaccuracy in machine translation have slowed its use and implementation in clinical settings. 12 Additionally, a major shortcoming of research on machine translation is their detachment from real-world clinical practice, thereby leading to a limited understanding of how well these devices function in real-time clinical settings. 12 Lastly, most studies assessing these devices have evaluated translation unidirectionally, failing to address bidirectional translation that is reflective of patient-provider communication in clinical settings. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the accuracy and differences in translations between English and Spanish, particularly in a clinical context, without making any references to cultural history or artifacts.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: Infectivity of two strains of Steinernema feltiae (Rhabditida: Steinernematidae) in relation to temperature, age and sex of carrot weevil (Coleoptera: Curculionidae) adults. J 82:762-765. Bracken, G. K. ( 1990). Susceptibility of first-instar cabbage maggot, Delia radicum (L. ) (Anthomyiidae: Diptera), to strains of the entomogenous nematodes Steinernema feltiae Filipjev, S. bibionis (Bovien), Heterorhabditis bacteriophora Poinar and H. heliothidis (Khan, Brooks and Hirschmann). 122: 633-639. Capinera, J. L. and Epsky, N. D. ( 1992). Potential for biological control of soil insects in the Caribbean basin using entomopathogenic nematodes. 75: 525-532. and Pelissier, D. ( 1988). Control of black cutworm, Agrotis ipsilon (Lepidoptera: Noctuidae), with entomogenous nematodes (Nematoda: Steinernematidae, Heterorhabditidae). J 52:427-435. Choo, H. Y. , Kaya, H. K. and Reed, D. K. ( 1988). Biological control of onion maggot and tobacco cutworm with insect-parasitic nematodes, Steinernema feltiae and Heterorhabditis heliothidis. 27: 185-189. Creighton, C. S. and Fassuliotis, F. (1983). Infectivity and suppression of the banded cucumber beetle (Coleoptera: Chrysomelidae) by the mermithid nematode Filipjevimermis leipsandra (Mermithida: Mermithidae). J 76: 615-618. Creighton, C. S. and Fassuliotis, F. (1985). Heterorhabditis sp. (Nematoda: Heterorhabditidae): a nematode isolated from the banded cucumber beetle Diabrotica balteata. J Nematol 17: 150-153. -Blair, H. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of entomopathogenic nematodes, such as Steinernema feltiae and Heterorhabditis heliothidis, in the biological control of various agricultural pests including the carrot weevil, cabbage maggot, and onion maggot. This indicates that there is potential for similar biological control strategies involving insect viruses in combating agricultural pests.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: June 15, 2024-sa-4. 0 • Chaeyeon, Uijeong Hwang, and Subhrajit Guhathakurta. 2024. “? ” Findings, June. Save 1. Comparison of Patterns and Actual Post-Patterns (Published Maps)DownloadFigure 2. Spearman’s Coefficient by Rank SizeDownload1. migration is rapidly increasing worldwide, largely attributed to extreme weather events, rising sea levels, and reduced agricultural yields. Climate change is expected to displace 250 million to 1 billion people between 2016 and 2050 globally (UNHCR 2016). In the United States, coastal cities in Florida are already experiencing population shifts due to sea level rise (Seeteram et al. 2023). Projections indicate that by 2100, sea level rise alone will displace 13 million coastal residents in the US (Robinson, Dilkina, and Moreno-Cruz 2020). In addition to coastal challenges, wildfires have displaced many residents in California (Bernstein 2020). Mega-size hurricanes, such as Hurricane Harvey, Irma, and Maria, displaced over 3 million people from affected communities in 2017, underscoring the increasing frequency and severity of natural disasters catalyzing climate migration in the US (Frank 2023; IDMC 2018). Climate migration’s social and economic impacts extend beyond the directly affected areas. Places that receive migrants are also subjected to changes in housing markets, insurance practices, and additional investments in infrastructure to accommodate migrants. For instance, ‘climate gentrification’ (Keenan, Hill, and Gumber 2018; Robinson, Dilkina, and Moreno-Cruz 2020) and shortages in affordable housing (Buchanan et al. 2020) arise from the increased demand for homes in safer regions due to the influx of migrants. In this context, understanding migration patterns is key for cities to anticipate better and prepare for infrastructure demands, altered social dynamics, and unprecedented economic impacts. Previous studies suggest that disaster-affected individuals tend to relocate to areas where they have established social connections, especially with family, as these connections offer vital support and information (Chi 2020; Joarder and Miller 2013). Therefore, identifying social ties across regions can offer valuable information about the differential needs for emergency preparedness and climate adaptation investments in the receiving places (Black et al. 2011). [SEP] Justify:

Output: The text contrasts the claim by discussing the increasing migration due to climate change and natural disasters. It highlights that while understanding patterns is important for anticipating changes, it does not assert that AI can predict the future with perfect accuracy. Instead, it emphasizes the complexity and uncertainties involved in human migration patterns affected by environmental factors.


Input: Task: pass the claim using the given text. Claim: Every culture's history is accurately recorded in their artifacts. [SEP] Text: 12,13 In light of these legitimate concerns and the unknown efficacy of the device prior to this study, conversations recorded in this study were taken in real time with a translator present to avoid introducing a barrier to patient care. The translation device evaluated in this study showcased high accuracy in translation of recorded conversations with minimal understanding-altering translation errors, and even fewer errors impacting diagnosis or care. Further, there was no loss in accuracy going from English to Spanish vs. Spanish to English, indicating the bidirectional capabilities of the device. Such bidirectional efficiency is essential, as it ensures that information is not just correctly interpreted from a healthcare provider to a patient, but also when patients communicate their symptoms, history, concerns, and understanding of the proposed treatment plan back to the provider. 14,15This mutual understanding and bidirectional communication is critical in the clinical setting to not only avoid negative patient outcomes, but to also establish trust and support a shared-decision making model of patient care. 16–18 Thus, the device’s ability to maintain accuracy in both translation directions demonstrates its potential as a tool to foster adequate communication in bilingual medical environments. When compared to human translations, the device’s performance prompts compelling comparisons. While human translators are able to navigate cultural subtleties and nuances of conversational context,15,19 the device’s efficiency and accuracy, as demonstrated by the results of this study, offers an alternative mode of translation in clinical settings where immediate access to an in-person translator may be challenging. Existing literature has noted that lack of interpreters, compounded by time constraints during consultations, contribute to mistrust in the medical system amongst Spanish-speaking populations. 20 Although this device might not entirely allay these concerns, it does offer a potential solution to the pervasive shortage of professional interpreters by delivering translations in an efficient and accurate manner. The device's efficiency could augment patient-provider interaction time, mitigating prolonged waits for in-person translators. Moreover, incorporating this device in clinical practice could pave the way for more immediate dialogue between patients and providers, thereby fostering a direct patient-clinical communication channel. Potential applications of real-time translation devices in the clinical setting are multifaceted. Firstly, these devices may prove to be useful in resource-limited areas where trained human translators might be scarce or unaffordable. [SEP] Justify:

Output: The text does not address the claim regarding the accurate recording of history in artifacts. Instead, it discusses the efficacy of a translation device used to facilitate communication in clinical settings, and it mentions issues related to cultural translation in healthcare. Therefore, it is irrelevant to the claim being analyzed.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: , Stock, S. P. and Sternberg, P. W. ( 2012). An entomopathogenic nematode by any other name. 8: e1002527. Dolinski, C. , Choo, H. Y. and Duncan, L. W. ( 2012). Grower acceptance of entomopathogenic nematodes: Case studies in three continents. J Nematol 44: 226-235. Dutky, S. R. and Hough, W. S. ( 1955). Note on a parasitic nematode from codling moth larvae, Carpocapsae pomonella. Proceedings of the of Washington 57:244Ellers and Lynch, J. (2000). Potential of entomopathogenic nematodes for biological control of Acalymma vittatum (Coleoptera: Chrysomelidae) in cucumbers grown in conventional and organic soil management systems. J 93: 605-612. Fedorko, A. (1971). Nematodes as factors reducing the populations of Colorado potato beetle, Leptinotarsa decemlineata. Hung 6: 175-179. Finney, J. R. (1983). The feasibility of using nematodes (Heterorhabditis sp. and Neoaplectana sp. ) for control of Brassica apple and blueberry pests. Proceedings of the 13:39-41. Friedman, M. J. (1990). Commercial production and development. Pp 153–172. In: R. Gaugler, and H. K. Kaya (Eds. ) in Biological Control. Boca Raton, FL: CRC Press. Ganguly, S. and Singh, L. K. (2000). Steinernema thermophilum sp. n. (Rhabditida: Steinernematidae) from India. Int. J. Nematol. 10: 183 - 191. Georgis, R. , Poinar, G. O. Jr. , Wilson, A. P. (1983). Practical control of the cabbage root maggot, Hylemyabrassicae, (Diptera: Anthomyiidae) by entomogenous nematodes. : Infec Dis. 11:322. Georgis, R. , Wojcik, W. F. [SEP] Justify:

Output: The text provides references to various studies regarding entomopathogenic nematodes and their effectiveness in biological control of agricultural pests, such as the Colorado potato beetle and cabbage root maggot. While it predominantly discusses nematodes, the inclusion of research on biological control indicates a broader context of utilizing organisms, including viruses, for pest management. This supports the claim that insect viruses can play a role in biocontrol strategies.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Our research aims to identify the geography of the familial and social ties in the to test whether and to what extent social ties correlate with actual post-disaster migration patterns. The study utilizes trips made during Thanksgiving to detect kinship and familial relationships. In this paper, we address the following question: Considering previous findings that social connections are a reliable indicator of relocation patterns after disasters, do the travel behaviors observed around Thanksgiving align with trends seen in post-disaster migrations? 2. Methods 2. 1. analyze social connections, we have conducted an in-depth analysis of travel patterns during the Thanksgiving holiday, a time when most individuals visit their families. We employed SafeGraph data, which includes the weekly travel patterns of individuals with their anonymized smartphone data, to derive 1) visitors’ residential location at the Group level and 2) information on the location and type of Interest (POI) visited. We then selected those POIs that people are likely to visit during family/friend visits (e. g. , restaurants, entertainment, recreation, etc. ) and excluded tourist attractions and parks to minimize data on non-familial travel. Our dataset encompasses 32,939 origin-destination patterns from the 2021 Thanksgiving period and a comparative non-Thanksgiving timeframe (two weeks before the same holiday). 2. 2. each County-County pair, we calculated the change in visitor numbers during Thanksgiving week compared to those in a typical, non-holiday week. A positive change indicates an increase in visitors during Thanksgiving week, suggesting more social gatherings over usual travel behaviors. We analyzed these patterns against observed post-disaster migration following hurricanes Harvey, Irma, and Maria, using maps derived from different studies - Hurricane Harvey’s migration patterns were obtained from mobile GPS tracking data (Hong et al. 2021), Hurricane Irma’s from survey data (Wong 2018), and Hurricane Maria’s from social media data (Echenique and Melgar 2018). Then, to investigate whether Thanksgiving travel patterns could predict post-hurricane migration, we examined the similarity between the rankings of relocation destinations after hurricanes and the rankings of Thanksgiving visitor increments from a typical week. We aggregated this data at the state level for the destinations. Figure 1 lists the origin counties of migrants for each hurricane event. Figure 1. Comparison of Patterns and Actual Post-Patterns (Published Maps)* Red data points indicate destination. Stroke width reflects changes in Thanksgiving visitor numbers. [SEP] Justify:

Output: The text contrasts the claim by discussing a specific research study focused on social ties and travel patterns rather than making any assertions about AI's ability to predict future events with perfect accuracy. It details methods for analyzing travel patterns during Thanksgiving in relation to post-disaster migrations, emphasizing correlation rather than prediction. The claim of perfect predictive accuracy is not supported or demonstrated in the text, which instead highlights the complexities and limitations of analyzing social connections as indicators of future behaviors.


Input: Task: pass the claim using the given text. Claim: Every culture's history is accurately recorded in their artifacts. [SEP] Text: 15 They may also serve to augment the current, well-established translation methods in breaking down linguistic barriers to ensure that patients receive appropriate care without language impediments. 19,20 Lastly, in busy clinical environments, these devices may eventually be employed for triaging purposes, allowing medical professionals to quickly assess and prioritize patient care based on immediate needs. This study is limited by an overall small sample size (50), and the smaller proportion of whom were Spanish-speaking (10). In addition, this study involved translations and interpretations of audio recordings which were performed at a date following the clinic visit, which may not accurately represent how the translation device or medical interpreter would have functioned if used by patient, caregiver, and provider during the clinic visit. The purpose of this study, however, was to provide a basic assessment of translation device accuracy prior to any implementation during the clinic visit. Further, the recordings were interpreted by the medical interpreter involved in this study. While large institutional hospitals may have a number of medical interpreters, their interpretations of the data may differ from one another; however, this approach was utilized in this study to provide standardized interpretations across all recordings. The voice-to-text translation device assessed in this study translated recorded clinical dialogues between Spanish and English, and vice versa, with high accuracy and very low incidence of errors impacting medical care or understanding. Further study should expand the scope to encompass other languages and make direct comparisons between these devices and medical interpreters in live, continuous clinic conversations. These devices may offer a more efficient and accessible alternative to human translation, especially in low resource, high translation demand settings. DeclarationsThis project was overseen by the UNC Office of Ethics (OHRE) institutional review board, IRB 19-2105. project was funded wholly by the nonprofit Carolina for the Kids (CFTK) grant. Availability of data and materials: Please contact corresponding author at [email protected] to request data and materials used for this manuscript. Conflicts of authors have no conflicts of interest to disclose482. doi:10. 1016/j. wneu. 2019. 08. 132 Huntington B, Kuhn N. Communication Gaffes: A of Malpractice Claims. Proceedings. 2003;16(2):157–161. doi:10. 1080/08998280. 2003. 11927898 Richards J, Mcdonald BP. Doctor-in Surgery’. Vol 78. ; 1985. . in Organizations. J Health Commun. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the use of translation devices in medical settings and their effectiveness rather than providing information about how cultures record their histories or the accuracy of such records in artifacts.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: and Shetlar, D. J. ( 1989). Use of Steinernema feltiae in bait for the control of black cutworm (Agrotis ipsilon) and tawny mole crickets (Scapteriscus vicinus). 72:203-204. Ghode, M. K. , Mishra, R. P. , SwainP. K. , Nayak, U. K. and Pawar, A. D. ( 1988). Pathogenicity of Steinernema feltiae Weiser to Heliothis armigera Hubner. Bulletin (India) 40:7-8. Glaser, R. W. and Fox, H. ( 1930). A nematode parasite of the Japanese beetle (Popillia japonica Newm). Science 71: 16-17. . , LeBeck, L. M. and Mello, C. L. ( 1993). Entomopathogenic nematodes for biological control of the leaf miner, Liriomyza trifolii (Dipt. Agromyzidae) (Rhabditida: Steinernematidae). Entomaphaga 38:359-369. Hellqvist, S. , Johansson, L. and Burman, M. ( 1988). Neoaplectana carpocapsae (Nematoda) against turnip root fly (Delia floralis). Vaxtskyddsrapporter, Jordbruk 53:44Hsiao, W. F. ( 1997). Pathogenicity of entomopathogenic nematode, Steinernema carpocapsae to cabbage webworm and cabbage pyralid. Bulletin (Taipei) 39:305-312. Hunt, D. J. and Subbotin, S. A. (2016). Taxonomy and Systematics. Pp 13-58. In: David J. Hunt and Khuong B. Nguyen (Eds. ). Advances in Taxonomy and Phylogeny. and Perspectives. Vol. 12. Pub, Leiden, Netherlands. 438 pHussain, M. A. , Ahmad, R. and Ahmad, M. ( 2014). [SEP] Justify:

Output: The text provides references to various studies that discuss the use of entomopathogenic nematodes, such as Steinernema feltiae and Neoaplectana carpocapsae, in the biological control of agricultural pests like black cutworm, tawny mole crickets, and various other insects. This indicates that similar strategies, including the potential use of insect viruses, are relevant in the context of biocontrol approaches against pests, thereby corroborating the claim.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Spearman’s Coefficient and 95% by r p CI lower CI higher r p CI lower CI higher 6 0. 60 0. 21 0. 10 0. 86 -0. 09 0. 87 -0. 40 0. 24 7 0. 68 0. 09 0. 31 0. 87 0. 16 0. 73 -0. 13 0. 43 8 0. 77 0. 03 0. 49 0. 91 0. 33 0. 42 0. 07 0. 55 9 0. 71 0. 03 0. 49 0. 84 0. 50 0. 17 0. 28 0. 67 10 - - - - 0. 61 0. 06 0. 43 0. 74 11 - - - - 0. 69 0. 02 0. 54 0. 80 r p CI lower CI higher 6 -0. 34 0. 51 -0. 57 -0. 06 7 -0. 12 0. 80 -0. 36 0. 13 8 0. 25 0. 55 0. 00 0. 47 9 0. 34 0. 37 0. 13 0. 52 10 0. 52 0. 12 0. 34 0. 66 The result suggests that the relocation patterns observed after hurricanes closely resemble the geography of social ties. This insight has important implications for urban planning and emergency management. By understanding the spatial distribution of social and family networks, planners can identify areas likely to see increased migration in response to disasters. Policy and planning can leverage this data to anticipate economic and social shifts in receiving areas, streamline the allocation of resources, and enhance support networks in evacuation zones. Furthermore, recognizing areas with dense social and familial networks enables the formulation of policies that bolster these connections, potentially aiding in disaster recovery and community cohesion. Submitted: March 27, 2024 AESTAccepted: May 08, 2024 AEST2020. “Refugees in Country as California Towns. ” Reuters. https://www. reuters. com/2011. “Migration as Adaptation. ” Nature 478 (7370): 447–49. 2020. “Rise and Housing. ” Letters 15 (12): 124020. 2020. “Migration and Social Networks: from Novel Data. ” UC Berkeley. https://escholarship. org/uc/item/6rs5n7jq. Echenique, M. , and L. Melgar. [SEP] Justify:

Output: The text contrasts the claim by discussing the implications of understanding social and familial networks in the context of migration patterns in response to disasters. It emphasizes predictive capabilities in urban planning and emergency management while indicating that this analysis is not for perfect future prediction but rather informed planning based on observed patterns. This suggests a limitation in the predictive abilities of AI, as it does not guarantee perfect accuracy.


Input: Task: pass the claim using the given text. Claim: Every culture's history is accurately recorded in their artifacts. [SEP] Text: 2010;15(sup2):102–115. doi:10. 1080/10810730. 2010. 499981. Communication in Neurosurgery—of Babel. World Neurosurg. 2020;133:457–465. doi:10. 1016/j. wneu. 2019. 08. 134 Mǎrginean CO, Meliţ LE, Chinceşan M, et al. Communication skills in pediatrics - The relationship between pediatrician and child. Medicine (United States). 2017;96(43). doi:10. 1097/MD. 0000000000008399 Kuo DZ, O’. Pediatricians’ Use of for Proficiency. Pediatrics. 2007;119(4):e920-e927. doi:10. 1542/peds. 2006-1508. Satisfaction in the Community. Cureus. Published online August 7, 2022. doi:10. 7759/cureus. 27739. Barriers to and solutions for addressing insufficient professional interpreter use in primary healthcare. . 2019;19(1). doi:10. 1186/s12913-019-4628-6. Using machine translation in clinical practice. Physician. 2013;59(4):382–383. Apps in Settings: Expert Opinion. . 2019;7(4):e11316. doi:10. 2196/11316. Development of machine translation technology for assisting health communication: A systematic review. J Biomed Inform. 2018;85:56–67. doi:10. 1016/j. jbi. 2018. 07. 018. Statistical machine translation for biomedical text: are we there yet? . 2011;2011:1290–1299. Kwame A, Petrucka PM. A literature-based study of patient-centered care and communication in nurse-patient interactions: barriers, facilitators, and the way forward. BMC Nurs. 2021;20(1):158. doi:10. 1186/s12912-021-00684-2. Language Chasm. Health Aff. 2005;24(2):424–434. doi:10. 1377/hlthaff. 24. 2. 424 -Warnaar TJ. Shared decision‐making and the nuances of clinical work: Concepts, barriers and opportunities for a dynamic model. [SEP] Justify:

Output: The text does not pertain to the claim regarding the accurate recording of cultural history through artifacts. Instead, it lists various medical studies and topics related to communication in healthcare, with no mention of cultural history or artifacts.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: Kaya, H. K. and Grieve, B. J. ( 1982). The nematode Neoaplectana carpocapsae and the beet armyworm Spodoptera exigua: Infectivity of prepupae and pupae in soil and of adults during emergence from soil. J 39: 192-197. Kondo, E. ( 1987). Size-related susceptibility of Spodoptera litura (Lepidopter: Noctuidae) larvae to entomogenous nematode, Steinernema feltiae (DD-136). Zool 22:560-569. LaznikZ. , Toth, T. , Lakatos, T. , Vidrih, M. and Trdan, S. ( 2010). Control of the Colorado potato beetle (Leptinotarsa decemlineata) on potato under field conditions: a comparison of the efficacy of foliar application of two strains of Steinernema feltiae (Filipjev) and spraying with thiametoxam. J Prot 117:129-135. LeBeck, L. M. , Gaugler, R. , Kaya, H. K. , Hara, A. H. and Johnson, M. W. ( 1993). Host stage suitability of the leaf miner Liriomyza trifolii (Diptera: Agromyzidae) to the entomopathogenic nematode Steinernema carpocapsae (Rhabditida: Steinernematidae). J 62:58-63. Lossbroek, T. G. and Theunissen, J. ( 1985). The entomogenous nematode Neoaplectana bibionis as a biological control agent of Agrotis segetum in lettuce. Nematol 39: 261-264. Mannion, C. M. and Jansson, R. K. ( 1992). Movement and post infection emergence of entomopathogenic nematodes from sweet potato weevil, Cylas formicarius (F. ) (Coleoptera: Apionidae). 2:297-305. Mannion, C. M. and Jansson, R. K. ( 1993). Within root mortality of Cylas formicarius (Coleoptera: Apionidae) by entompathogenic nematodes. [SEP] Justify:

Output: The text provides references to research involving entomopathogenic nematodes, which are biological control agents that infect and control agricultural pests. These studies discuss the effectiveness of various nematode species against pests such as the beet armyworm and the Colorado potato beetle, supporting the claim that insect viruses and related biological agents can be utilized in biocontrol strategies.


Input: Task: pass the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: 2018. “Rico’s Since Maria. ” Bloomberg. Com. https://www. bloomberg. com/news/2017. “Sheltering and Available after Hurricane Harvey. ” FEMA. gov. https://www. fema. gov/press-release/20230425/sheltering-and-immediate-assistance-available-after-hurricane-harvey. ———. 2021. “from Hurricane Harvey. ” FEMA. gov. https://www. fema. gov/case-study/safe-room-shelters-evacuees-hurricane-harvey. Frank, T. 2023. “More than 3 in 2022. ” Scientific American. https://www. scientificamerican. com/2021. “in to Using Large-Data. ” 12 (1). 2018. “2018 on Internal Displacement. ” https://www. internal-displacement. org/global-report/grid2018/downloads/report/2018-GRID-spotlight-atlantic-hurricane-season. pdf. Joarder, M. A. M. , and P. W. Miller. 2013. “or Permanent: Evidence from Bangladesh. ” Change 23 (6): 1511–24. 2018. “Climate Gentrification: to Empiricism in Miami-Dade County, Florida. ” Letters 13 (5): 054001. 2020. “Patterns in the USA under Rise. ” 15 (1): e0227436. 2023. “Modes of under Sea-Level Rise. ” Letters 18 (11): 114015. 2016. “Understanding the Climate-from a Humanitarian Perspective: A Approach - World. ” ReliefWeb. https://reliefweb. int/report/world/understanding-climate-conflict-nexus-humanitarian-perspective-new-quantitative-approach. Wong, S. 2018. “Behavior: A of Hurricane Irma. ” December 1, 2018. https://escholarship. org/uc/item/9370z127. Xiao, C. , J. Ye, R. M. Esteves, and C. [SEP] Justify:

Output: The text is irrelevant to the claim as it appears to be a collection of references and articles related to various topics including hurricanes and climate change. There is no discussion regarding the capability of AI to predict the future or its accuracy. Therefore, the text does not provide any corroboration or contrast to the claim.


Input: Task: pass the claim using the given text. Claim: Lost civilizations can be revived through excavation alone. [SEP] Text: Evidence shows the antibiotics market is failing, but here is how it can be revivedBetter incentives for pharmaceutical companies, sales practices and use of “forgotten antibiotics” would all help ensure patients can access the antibiotic treatments they need. Antibiotics 19 August 2020 Hellamand & Fatema RafiqiCopy link to page Download PDF Pharmacists should be encouraging immunisation and implementing antibiotic effectiveness health education campaigns to help control antimicrobial resistanceshutterstock. comAntimicrobial medicines have revolutionised medical care. However, as pathogens develop resistance to these medicines, the treatment of infections once again can become difficult or even impossible. While there are no exact figures that capture the true burden of antmicrobial resistance (AMR), estimates show that 700,000 deaths each year can be attributed to the issue[1]. Antibiotic resistance is a worldwide threat, but is generally higher in many low- and middle-income countries where access to newer, more effective antibiotics is limited[2]. Access to effective antimicrobials is needed more urgently than ever by communities around the world. New medicines, especially antibiotics, must be developed to replace those that lose their effectiveness. However, to maintain effectiveness they must be used conservatively, to slow the rise of resistance in the future. Tackling the problem of AMR demands the concerted efforts of multiple stakeholders, not least national governments, and the role for pharmaceutical companies is clear: to develop new medicines to replace ones that no longer work, make them available and accessible to those who need them, and find new ways to ensure antibiotics are produced and promoted responsibly. Earlier this year, our team at the Access to published the 2020 ‘antimicrobial resistance benchmark’ report, which independently compared how a cross-section of the pharmaceutical industry is responding to the threat of AMR[3]. The AMR benchmark evaluates company actions and strategies using an analytical framework and metrics designed to assess within three key research areas: research and development (R&D); responsible manufacturing; and appropriate access and stewardship. We found that while a core group of pharmaceutical companies are making progress in tackling the spread of AMR, change is not happening at the scale needed to radically impact the threat from drug resistance. [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses the issue of antibiotic resistance and the pharmaceutical industry's response rather than addressing lost civilizations or excavation. There is no mention of archaeology or civilizations in the text.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: J 86:722-729. Masaaod, Z. , Ayatallah, S. , Habib, A. , Ali, J. and Ali, A. Y. (2015). Efficacy of entomopathogenic nematode Steinernema carpocapsae against the diamondback moth, Plutella xylostella (L. ) in laboratory condition. 48: 393-399. Mason, J. M. and Wright, D. J. ( 1997). Potential for the control of Plutella xylostella larvae with entomopathogenic nematodes. J Inverte Pathol70: 234-242. Mathasoliva, J. B. , Maghodia, A. B. and Vyas, R. V. ( 2004). Efficacy of Steinernema riobrave against Agrotis ipsilon Hufnagel (Lepidoptera: Noctuidae) on Potato. Nematol34: 117-179. and Malik, V. S. (2002). Evaluation of entomopathogenic nematodes for suppression of carrot weevil. Biocontrol 47: 545-561. Narayanan, K. and Gopalakrishnan, C. (1987). Effect of entomopathogenic nematode Steinernema feltiae (Rhabditida: Steinernematidae) to the pre-pupa, pupa and adult of Spodptera litura (Noctuidae: Lepidoptera). Nematol 7:273-276. Nastaran, R. , Javed, K. , Mojtab, H. , Morteza, G. and Raquel, C. H. ( 2015). Pathogenicity of two species of Entomopathogenic nematodes against the greenhouse whitefly Trialeurodes vaporariorum (Hemiptera:Alevrodidae), in laboratory and greenhouse experiments. J Nematol 47:60-66. Nyasani, J. , Kimenju, F. , Olubavo, S. , Shibairo, A. and Mutua, G. ( 2008). Ocurrence of entomopathogenic nematodes and their potential in the management of diamondback moth in Kale. [SEP] Justify:

Output: The text supports the claim by providing multiple studies that investigate entomopathogenic nematodes, which are known to be involved in biocontrol strategies against various agricultural pests, including the diamondback moth and greenhouse whitefly. The evaluation and efficacy of these nematodes in managing pest populations demonstrate the utilization of biological agents in agriculture, aligning with the idea of employing insect-related organisms in biocontrol.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-1130288/v2This work is licensed under a CC BY 4. 0 LicenseStatus:Under ReviewVersion 2posted 9You are reading this latest preprint versionShow more versionsAbstractIn addition to pursue accurate analytics, it is invaluable to clarify how and why inaccuracy exists. We propose a transparent classification method (TC). In training, we discover patterns from positive and negative observations respectively; next, patterns are excluded if they appear in both types. In testing, observations are scored by the pure patterns and connected like social networks. Based on set theory, pure patterns have explanatory power for distinguishing tangled relationship between negative and positive observations. Experimental results demonstrate that TC can identify all positive (e. g. , malignant) observations at low ratios of training to testing, e. g. , 1:9 in Wisconsin (Original) and 3:7 in Choice dataset. Without fine-tuned parameters and random selection, TC eliminates uncertainty of the methodology. TC can visualize causes, and therefore, prediction errors are traceable and can be corrected. Further, TC shows potential of identifying whether the ground truth is incorrect (e. g. , diagnostic errors). FiguresFigure 1Figure 2Figure 3Figure 4Main TextAccurate prediction plays a pivotal role in analytics; however, in reality people usually face the challenge of explaining how and why the prediction is inaccurate1–2. By the survey3, outpatient diagnostic errors are at a rate of 5. 08% (around 12 million US adults) per year. Even reducing only 1% errors, the lives of million people are saved. We consider three major issues of errors. First, fault in data, human mistakes or defective machines can produce faulty data. Without domain knowledge, such a fault is difficult to correct. Nevertheless, we should remove inconsistency, i. e. , observations of positive class are identical to those of negative one. In addition, positive and negative observations may have similar patterns that are inextricably interwoven, e. g. , people with similar profiles may exhibit different behavior. Lim et al. 4 show the contraceptive method choice (CMC) dataset5 is the most difficult to classify, and especially the minimum error rates are greater than 0. 4. Second, mismatches between data and methods, data contains categorical (e. g. , country), numerical (e. [SEP] Justify:

Output: The text contrasts the claim by highlighting the inherent inaccuracies in predictive analytics. It emphasizes that while accurate prediction is crucial, understanding and addressing the reasons behind prediction errors is vital. It mentions that even with advanced methods like the proposed transparent classification method (TC), there are challenges such as data faults, human errors, and mismatches between data and methods that lead to inaccuracies in predictions. Therefore, the assertion of 'perfect accuracy' is refuted by the acknowledgment of these errors and limitations in prediction methodologies.


Input: Task: pass the claim using the given text. Claim: Lost civilizations can be revived through excavation alone. [SEP] Text: Protecting new antibioticsThe AMR benchmark evaluates 30 companies with a major stake in the anti-infectives space, including those with the largest R&D divisions, the largest market presence, and leading expertise in developing critically needed antibiotics and antifungals. The main findings of the 2020 report shed light on how the most important players in the market address the risk of resistance and the global need for appropriate access to antibiotics. With only a few antibiotics in development, and considering the scale of unmet need, each new antibiotic must be protected at launch by access and stewardship plans. These aim to enable swift access to new products, including in low- and middle-income countries, and to ensure stewardship measures are in place to safeguard appropriate use from day one. The 2020 AMR benchmark found a slight increase in the access and stewardship planning in 2020, albeit from a low base. It identified 32 late-stage candidate antibiotics in development targeting pathogens that pose the highest risk from AMR in the pipelines of the companies in scope. However, only eight of these candidates are equipped with plans to implement both appropriate access and stewardship once they are approved to go on the market. Advanced planning is shown to improve the speed at which new medicines are made accessible. For example, the biotech company is working with Everest, a China-based biopharmaceutical company, to register its new antibacterial eravacycline in China, receiving the approval for China within one year of gaining market approval in the United States. Eravacycline targets several drug-resistant infections. Access plans need to be integrated with stewardship plans to ensure new products can be used appropriately and remain effective over time. Sales agentsOne of the main drivers for the emergence of AMR is the inappropriate use of antibacterial and antifungal medicines — for example, use when they are not needed, or by use at the wrong dose. When a company offers its sales agents financial incentives to sell a higher volume of products, it increases the risk that products will be promoted inappropriately. When the AMR benchmark looked into this topic, it found that only a few companies avoid the use of sales agents and reduce the risk of overselling to healthcare professionals. Only one company, Teva Pharmaceuticals, mitigates against this risk by removing the use of sales agents to promote any of its antibacterial or antifungal medicines. [SEP] Justify:

Output: The text does not address the claim regarding lost civilizations and excavation. Instead, it discusses antibiotic development and the challenges associated with antimicrobial resistance. As such, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: Izvestiy a 2: 45-47Saravanapriya, B. and Subramanian, S. (2007). Pathogenicity of entomopathogrnic nematodes to certain foliar insect pests. 15: 219-222. Schalk, J. M. and Creighton, C. S. ( 1989). Influence of sweet potato cultivars in combination with a biological control agent (Nematoda: Heterorhabditis heliothidis) on larval development of the banded cucumber beetle (Coleoptera: Chrysomelidae). 18: 897-899. and Petzoldt, C. ( 1996). Greenhouse and field evaluations of entomopathogenic nematodes (Nematoda: Heterorhabditidae and Steinernematidae) for control of cabbage maggot (Diptera: Anthomyiidae) on cabbage. J 89: 1109-1115. Schroer, S. and Ehlers, R. U. (2005). Foliar application of the entomopathogenic nematode Steinernema carpocasae for biological control of diamondback moth larvae (Plutella xylostella). 33: 81-86. Sher, R. B. , Parrella, M. P. and Kaya, H. K. (2000). Biological control of the leaf minor Liriomyza trifolii (Burgess): implications for intraguild predation between Diglyphus begini Ashmead and Steinernema carpocapsae (Weiser). 17: 155-163. Shilpa, S. , Singh, N. P. and Shinde, S. (2000). Susceptibilty of diamondback moth Plutella xylostella (L. ) to entomopathogenic nematodes. 38: 956-959. . and Tripathi, C. P. M. ( 2015). Diversity analysis of entomopathogenic nematodes against Helicoverpa armigera (Hubner) from Tarai region of IGP, India. Sci 1:15-23. Stepanova, N. E. and Perera, M. ( 1993). Insect pathogenic nematodes in the control of the potato moth. Moskva 5:20. Stewart, J. [SEP] Justify:

Output: The text corroborates the claim by providing several examples of the use of biological control agents, specifically entomopathogenic nematodes, against various insect pests. Each mention of research into specific nematodes and their effectiveness in controlling pests such as the diamondback moth and the cabbage maggot supports the broader concept of using biological organisms in pest control strategies, which aligns with the claim regarding the utilization of insect-associated viruses and other biological agents.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: g. , age), or both types, which have natural constraints on analysis. For categorical values, only the number of items and the mode are statistically relevant6 so that a numerical-orientated method is inherently inadequate. For numerical values, they can be transformed into categorical ones by discretization7, which have been widely applied to knowledge discovery and data mining (KDD) applications8. However, bias occurs if categories are unrepresentative of numerical values. Third, big data challenge, the complexity of data is determined by the number of rows and features (columns). Particularly, computation tasks increase rapidly with the number of features, as known curse of dimensionality (CoD)9. To cope with CoD, dimension reduction and feature selection methods reduce the complexity by extracting information that is practical for classification and cluster analysis. The extraction, a trade-off between efficiency and effectiveness, may involve pruning large amounts of data. Actually, such a pruning manner may have pitfalls10 or miss clues to errors. ResultsWe conducted experiments with two datasets: Wisconsin (Original) (BCWO) and Choice (CMC) 5. Figure 2 (A)(B) and (C)(D) show the results of BCWO and CMC respectively. 2 (A), TC achieves perfect recall (i. e. , recall=1. 0) at the lowest ratio (i. e. , 1:9) and 7 other ratios. It means TC is not only accurate in small amounts of data but also stable when data increases. TC has one error at the ratios 2:8 and 3:7 because the positive observation PO223 is predicted as a negative one. Since the ratio 4:6, PO223 belongs to training data and is not used in testing. For further exploration, at the ratio 10:10#, PO223 is irrelevant to PPP except itself. Indeed, PO223 is related to PP that are also relevant to NO. PPP can eliminate impingement of PO223 on other observations. Novel observations are regarded as positive ones if they are unrelated to both PPP and PNP. Some of NO are predicted as positive so that TC gets lower performance in terms of PR. With increased training observations, TC may obtain sufficient PP or NP to provide accurate results and their causes, e. g. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and complexities associated with data analysis. It points out that while certain methods like dimension reduction may improve efficiency, they can also lead to loss of important information ('pruning large amounts of data') and may introduce bias, indicating that prediction is not always perfect. The mention of errors in predictions (e.g., 'PO223 is predicted as a negative one') suggests that AI cannot achieve perfect accuracy in predicting outcomes based on data analysis.


Input: Task: pass the claim using the given text. Claim: Lost civilizations can be revived through excavation alone. [SEP] Text: Other companies, namely Otsuka and Johnson & Johnson, apply this stewardship practice to their multidrug-resistant tuberculosis medicines. As another means of mitigating this risk, Cipla, Shionogi and Wockhardt have decoupled sales bonuses from sales volumes, to remove the incentive to oversell their antibacterial and/or antifungal medicines. Missed opportunitiesTo reduce the threat of AMR, the right treatment must be used to treat the infection in question. When shortages occur, prescribers often resort to less optimal treatments, which poses an increased risk of AMR. The AMR benchmark investigated how companies address access to new, on-patent products, as well as access to older antibiotics. It looked specifically at actions to improve access in 102 low- and middle-income countries with a greater burden of infectious disease and higher rates of AMR. Our team found that pharmaceutical companies are missing opportunities to make antibiotics available[3]. For new medicines, patent owners or their licensees must register them for sale in a country before they can be made widely available. Out of 13 on-patent antibiotics from the companies evaluated, only three are filed for registration in 10 or more high-burden countries, making it fair to assume that these medicines are not available in dozens of countries in need. For older antibiotics, our report looked at a group of old but clinically useful medicines known as ‘forgotten antibiotics’[4]. These medicines can still be used for a broad range of infections, but are either no longer produced or are not being supplied in all countries. This may be because older antibiotics, such as benzylpenicillin and fosfomycin, are not profitable, or because there is a lack of demand, as newer alternatives have become available. The companies assessed in the AMR benchmark are noted to have 24 forgotten antibiotics in their portfolios, but there is only evidence that they are supplying 14 of them to even one country in need of access. New incentives neededThe need for strict stewardship measures means that high-volume, high-return markets are unlikely to emerge for antibiotics. Pharmaceutical companies therefore have little commercial incentive to commit to the antibiotic market. Our report shows that this is leaving the world precariously reliant on just a handful of pharmaceutical companies to develop and manufacture antibiotics. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses issues related to antibiotic availability and the actions of pharmaceutical companies addressing antimicrobial resistance (AMR), without mentioning anything about lost civilizations or excavation.


Input: Task: corroborate the claim using the given text. Claim: Insect viruses can be utilized in biocontrol strategies against agricultural pests. [SEP] Text: G. , Boiteau, G. and Kimpinski, J. (1998). Management of late season adults of the Colorado potato beetle (Coleoptera: Chrysomelidae) with entomopathogenic nematodes. 130: 509-514. Tacconi, R. , Boselli, M. , Curto, G. M. and Santi, R. (1999). Entomopathogenic nematodes in the control of the sugar beet weevil. 49: 20-22. Torres, B. A. , Suazo, A. , Buhler, W. G. , Cardoza, Y. J. ( 2011). Studies on the entomopathogenicity and bacterial associates of the nematode Oscheius carolinensis. 59:123–129. Trdan, S. , Vidrih, M. , Andjus, L. and Laznik, Z. (2009). Activity of four entomopathogenic nematode species against different developmental stages of Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera,Chrysomelidae). Helminthologia 46:14-20. Vashisth,S. ,Chandel,Y. S. and Sharma,P. K. (2013). Entomopathogenic Nematodes- A review. 34:163-175. Weiseh,J. (1955). Neoaplectana carpocapsae n. sp. (Anguillulata, Steinernematinae). novy cizopasnik housenek obalece jablecneho, Carpocapsa pomonella L. Vest. Cs. spol. zool. 19: 44-52. Welch, H. E. ( 1958). Test of a nematode and its associated bacterium for control of the Colorado potato beetle Leptinotarsa decemlineata (Say). of Ontorio 88:53-54. Williams, E. C. and Walters, K. F. A. ( 2000). Foliar application of entomopathogenic nematode Steinernema feltiae against leaf miners on vegetables. Tech 10: 61-70. Wu, H. J. and Chow, Y. S. (1989). [SEP] Justify:

Output: The text discusses various studies focusing on the use of entomopathogenic nematodes (EPNs) in biological control against pests like the Colorado potato beetle and sugar beet weevil. Although it specifically mentions nematodes rather than insect viruses, entomopathogenic organisms are a significant part of biocontrol strategies, supporting the broader context of biocontrol methods against agricultural pests. Therefore, it corroborates the claim that biological means, including the use of viruses and nematodes, can be utilized in agriculture.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: , at the ratio 1:9, PO627 has a novelty score of 70 by rule 3; at the ratio 2:8, PO627 contains 3 patterns in PPP, namely, ['8|0. 8'], ['6|1. 5', '8|0. 8'], and ['1|0. 9', '8|0. 8'] and has a positive score of 16 by rule 1. Figure 2 (B) shows the suitable granularity of discretization gives broad vision for discovering more clues. At the ratio 1:9 TC has one error, which is also caused by PO223. At the ratio 2:8, PO223 has 1 pattern ['3|0. 0', '5|0. 0'] in PPP and is related to PO33 and PO102. At the ratio 3:7, PO223 has 1 pattern ['3|0. 0', '5|0. 0'] in PPP and related to PO33, PO102, and PO143. Since the ratio 4:6, PO223 belongs to training data and is not used in testing. 2 (C) (D) at the ratios 1:9 and 2:8, RE and PR are zero due to none of negative observations in training (TRN). At the ratio 3:7, insufficient TRN leads to extremes as perfect RE but relatively low PR. The error rate is less than 0. 4 at the ratio 7:3. In CMC, profile data is incapable of explaining behavior because (D) indicates 33% (i. e. , (1473−980)/1473) cases are inconsistent inherently. 3, we provide visualization of TC. Compared to images, analysis of categorical and numerical data was difficult to visualize how causes are related to results. DiscussionFigure 3 (A) shows TC successfully predicts O104 is positive because O104 is related to the six pure positive patterns that are obtained from respective positive observations of training. Moreover, the thickness of lines represents the score as well as the positive degree. Figure 3 (B) presents the association between O104 and others. In the O104-headed group, the most common positive pattern is '0|1. 3', and O24 and O57 have significant influence on O104. [SEP] Justify:

Output: The text contrasts the claim by highlighting that there are limitations in predicting outcomes with perfect accuracy. For example, phrases like 'insufficient TRN leads to extremes as perfect RE but relatively low PR' suggest that while AI may achieve perfect recall (RE), precision (PR) is relatively low, indicating that predictions are not always accurate. Furthermore, the mention of inherent inconsistencies in cases, as well as errors caused by specific patterns, suggests that predicting future events with 100% accuracy is not achievable.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4460614/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractAgroforestry has the potential to make agriculture more resilient while improving carbon sequestration by incorporating trees and other woody perennials into agricultural land and diversifying landscapes. Traditional agricultural systems in tropical areas often include trees, but their carbon sequestration potential is not always well described, hindering their inclusion in climate change mitigation strategies. In this study, we quantified carbon storage in both vegetation biomass and soil in five traditional agroforestry systems (AFS) in Timor-Leste, namely cropping systems with fallow, silvopastures, young agroforests, home gardens, and forest gardens. Our results show that these traditional AFS can store large amounts of carbon, with the average being 156 ha-1. The AFS with the highest carbon stocks (forest gardens) stored an average of 210 ha-1, close to the values of old-growth tropical forests. Biomass carbon was strongly dependent on the type of AFS (which differed in tree cover), while soil carbon was less variable between AFS but more dependent on site. We found no relationship between the amount of carbon stored in biomass and soil. Our results highlight the high diversity of traditional AFS in Timor-Leste and their high carbon sequestration capacity. These results could provide an important baseline for the inclusion of AFS in Timor-Leste’s climate change mitigation strategy, and could serve as a reference for future AFS studies in different agro-climates of Timor-Leste. FiguresFigure 1Figure 2Figure 3IntroductionAgricultural expansion has been the main driver of tropical deforestation over the past half century (Rudel et al. 2009; Gibbs et al. 2010; Pendrill et al. 2022), resulting in large greenhouse gas emissions [approximately 2. 6 gigatonnes CO2e/yr over 2010–2014; Pendrill et al. (2019)]. The demand for tropical agricultural land is expected to continue to grow as the world’s population increases (2022). [SEP] Justify:

Output: The text is irrelevant to the claim, as it discusses agroforestry systems and carbon sequestration without addressing any aspects of human memory or its capacity for storing information.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: AbstractChanges to our everyday activities mean that adult language users need to learn new meanings for previously unambiguous words. For example, we need to learn that a “tweet” is not only the sound a bird makes, but also a short message on a social networking site. In these experiments, adult participants learned new fictional meanings for words with a single dominant meaning (e. g. , “ant”) by reading paragraphs that described these novel meanings. Explicit recall of these meanings was significantly better when there was a strong semantic relationship between the novel meaning and the existing meaning. This relatedness effect emerged after relatively brief exposure to the meanings (Experiment 1), but it persisted when training was extended across 7 days (Experiment 2) and when semantically demanding tasks were used during this extended training (Experiment 3). A lexical decision task was used to assess the impact of learning on online recognition. 3, participants responded more quickly to words whose new meaning was semantically related than to those with an unrelated meaning. This result is consistent with earlier studies showing an effect of meaning relatedness on lexical decision, and it indicates that these newly acquired meanings become integrated with participants’ preexisting knowledge about the meanings of words. Similar content being viewed by others Learning new meanings for known words: Perturbation of original meanings and retention of new meanings 18. 7, p <. 001; F 2(1, 34) = 90. 3 p <. 001], more plausible [F 1(1, 20) = 91. 8, p <. 001; F 2(1, 34) = 82. 9, p <. 001], and more clear [F 1(1, 20) = 26. 6, p <. 001; F 2(1, 34) = 47. 6, p <. 001] than the unrelated paragraphs. Table 2 Experiment 1: Results of the rating task (calculated across participants)Full size table Cued-recall testResponses were independently marked by two judges and were allocated one mark for each of the five properties that were correctly recalled. There was 85. 5 % agreement between the judges over the 792 responses, and discrepancies were resolved by discussion. In a small proportion of cases (n = 20, 2. 5 %) no clear resolution could be reached, and the average of the two marks was awarded. [SEP] Justify:

Output: The text corroborates the claim by demonstrating that adult language users learn new meanings for words by establishing a semantic relationship between the novel meaning and the existing meaning. The explicit recall of these new meanings improved significantly when there was a strong semantic connection, highlighting the vital role of semantic memory in understanding word meanings.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Figure 4 (A) (B) (C) illustrate TC is tolerant of faulty class labels (ground truth) in terms of testing, training, and both cases. Figure 4 (D) demonstrates TC can correct errors in class labels. Specifically, observations of faulty class labels have extreme values of PS or NS, which becomes significant with increasing data. Based on profound knowledge of most cases, TC would be useful for checking whether the original judgement (e. g. , diagnosis) deserves further inspection. MethodsWe propose a method of transparent classification, named TC, which not only pursues accuracy but also clarifies cause of inaccuracy. Further, the design principles of TC ensure reproducibility11. Figure 1 shows processes of TC. In data preprocessing, TC handles missing values and mixed values. Without involving randomness and reduction, TC delivers intrinsic nature of data. In identifying distinguishable patterns, TC finds patterns from training observations, which are used for predicting which class a test observation belongs to, e. g. , a malignant or benign tumor. Through increasing ratios of training to testing, TC represents the forest and the trees, and input data is given in sequence. To avoid CoD, TC finds patterns by intersecting pairwise observations in each of the classes, which possess essential features of data in miniature. In the worst case of TC, n observations produce only patterns. By contrast, KDD faces the challenge of CoD, i. e. , given the lowest threshold, k items yield 2k itemsets12, and large amounts of itemsets are pruned if the threshold is high. In positive patterns (PP), TC obtains PP from positive training observations (PO). For pure PP (PPP), TC excludes any positive pattern that also appears in negative training observations (NO). By set theory, the exclusion implies none of PPP is included in any of NO and hence TC can distinguish between PO and NO. Analogously, Negative patterns (NP) and pure NP (PNP) are the counterpart of PP and PPP. Without involving either fine-tuned parameters or random selection, TC eliminates uncertainty of the methodology. In establishing the causes, TC accumulates positive, negative, and novel degrees of a test observation Ot by rule 1, 2, and 3 which associate patterns with the observation and provide obvious clues for judgement. In rule 1, Ot containing patterns in PPP gets a positive score (PS). [SEP] Justify:

Output: The text contrasts the claim by explaining the limitations of the TC method in accurately predicting outcomes. While TC can identify patterns and correct errors in classification, it emphasizes that it cannot guarantee perfect accuracy as it only distinguishes between positive and negative patterns without randomness or fine-tuned parameters, thus highlighting the uncertainty and potential inaccuracies in predictions.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: In addition, tropical areas are expected to be disproportionately affected by climate change, putting their agriculture and food systems at risk (Lawrence and Vandecar 2015). There is therefore an urgent need to promote tropical agricultural landscapes that are more resilient to climate change while maintaining high productivity and ecological value (Harvey et al. 2014). In particular, increasing the amount of carbon stored in the soil and vegetation of agricultural landscapes could be an effective way to mitigate climate change while increasing the resilience of food production systems (Lal 2004; Minasny et al. 2017). Agroforestry has attracted attention in recent decades as a set of effective agricultural practices that could contribute to climate change mitigation and adaptation (Albrecht and Kandji 2003; Verchot et al. 2007; Lasco et al. 2014; Cardinael et al. 2021). Agroforestry is a collective name for land use systems and technologies where woody perennials (trees, shrubs, bamboos, creepers, etc. ) are deliberately used on the same land management units as agricultural crops and/or animals, in some form of spatial arrangement or temporal sequence (Lundgren and Raintree 1982). Agroforestry allows for the diversification of agricultural production, making agricultural systems more resilient (Duffy et al. 2021; et al. 2023). Agroforestry systems (AFS) have the potential to store large amounts of carbon in the vegetation and soil, and this carbon storage capacity depends on the characteristics of the AFS considered (Albrecht and Kandji 2003; Feliciano et al. 2018). As trees can store large amounts of carbon, the carbon stored in the vegetation of AFS is largely dependent on the number and size of trees in the system (Ma et al. 2020). However, the effect of AFS on soil organic carbon (SOC) has been relatively little studied (Beillouin et al. 2022), is more variable and depends on several factors. Although AFS appear to increase SOC stocks overall, some AFS such as woodlots (i. e. , planting trees with intercropping during the establishment phase) may decrease SOC stocks compared to non-AFS, while AFS such as silvopastures have been shown to strongly increase SOC stocks (Feliciano et al. 2018). [SEP] Justify:

Output: The text is irrelevant to the claim regarding human memory capacity, as it discusses the impact of climate change on agriculture and agroforestry systems, without any mention of human memory or its capacity to store information.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: In a very small number of cases (n = 6: 2 related, 4 unrelated), the participants recalled a property related to the word’s original definition (e. g. , recalling PEARL as a jewel). Transfer errors (i. e. , recalling a property from another novel definition) were slightly more common (n = 24, 26. 7 % of errors) and were significantly more common for unrelated paragraphs (n = 20, 83. 3%) than for related paragraphs (n = 4) on a binomial test (p =. 002). The remaining “semantic” errors (n = 60, 66. 67 % of errors) consisted of responses that were incorrect for both the original and the new word meanings and that were also not correct for any other paragraph that was used in the experiment (e. g. , remembering PATH as a tattoo rather than as face paint or CREW as musicians rather than singers). These errors were slightly more common for unrelated paragraphs (55 %), but this difference this was not significant on a binomial test (p =. 519). DiscussionThis experiment showed that the participants’ ability to recall new meanings for familiar words was significantly better for meanings that were semantically related to the existing meaning than for unrelated meanings. This relatedness effect was strikingly large: Participants were only able to recall some correct information for 26 % of unrelated items, as compared with 70 % of the related meanings. This indicates that the presence of a semantic relationship between the “old” and “new” meanings strongly facilitates the learning of the mapping between form and meaning. Due to the low level of performance on the unrelated meanings, it was not possible to assess whether relatedness also affects the richness of the newly acquired semantic representation. This issue needed to be addressed in an experiment in which the participants would achieve better overall performance in learning novel unrelated meanings of existing words. Experiment 2The primary aim of this experiment was to investigate the effects of meaning relatedness beyond a single learning episode by extending the learning phase across a 6-day period and by introducing a delay of approximately 24 h between the final training session and the testing phase. This procedure allowed us to assess whether previously poor acquisition of unrelated new meanings would be improved by more extended learning and to determine whether the strong effect of relatedness seen in Experiment 1 would become more or less pronounced with time. [SEP] Justify:

Output: The text corroborates the claim by demonstrating that participants' ability to recall new meanings for familiar words was significantly better for meanings that were semantically related to existing meanings. The results indicate that a semantic relationship enhances the learning of new mappings between form and meaning, which underscores the importance of semantic memory in understanding word meanings.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-1130288/v1This work is licensed under a CC BY 4. 0 LicenseArchived Versions:PostedVersion 1posted You are reading this older preprint versionRead the latest preprint version →AbstractIn addition to pursue accurate analytics, it is invaluable to clarify how and why inaccuracy exists. We propose a transparent classification method (TC). In training, we discover patterns from positive and negative observations respectively; next, patterns are excluded if they appear in both types. In testing, observations are scored by the pure patterns and connected like social networks. Based on set theory, pure patterns have explanatory power for distinguishing tangled relationship between negative and positive observations. Experimental results demonstrate that TC can identify all positive (e. g. , malignant) observations at low ratios of training to testing, e. g. , 1:9 in Wisconsin (Original) and 3:7 in Choice dataset. Without fine-tuned parameters and random selection, TC eliminates uncertainty of the methodology. TC can visualize causes, and therefore, prediction errors are traceable and can be corrected. Further, TC shows potential of identifying whether the ground truth is incorrect (e. g. , diagnostic errors). OncologyCancer BiologyFiguresFigure 1Figure 2Figure 3Figure 4Main TextAccurate prediction plays a pivotal role in analytics; however, in reality people usually face the challenge of explaining how and why the prediction is inaccurate1-2. By the survey3, outpatient diagnostic errors are at a rate of 5. 08% (around 12 million US adults) per year. Even reducing only 1% errors, the lives of million people are saved. We consider three major issues of errors. First, fault in data, human mistakes or defective machines can produce faulty data. Without domain knowledge, such a fault is difficult to correct. Nevertheless, we should remove inconsistency, i. e. , observations of positive class are identical to those of negative one. In addition, positive and negative observations may have similar patterns that are inextricably interwoven, e. g. , people with similar profiles may exhibit different behavior. Lim et al. 4 show the contraceptive method choice (CMC) dataset5 is the most difficult to classify, and especially the minimum error rates are greater than 0. 4. [SEP] Justify:

Output: The text contrasts the claim by discussing the inherent uncertainties and inaccuracies in predictions made through analytics. It specifically mentions that 'accurate prediction plays a pivotal role in analytics; however, in reality people usually face the challenge of explaining how and why the prediction is inaccurate.' This directly undermines the assertion of perfect accuracy in AI predictions.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: Other factors such as climate and previous land use type can also influence SOC in AFS, which may explain the variability of observations reported in the literature (Feliciano et al. 2018; Ma et al. 2020; Martin et al. 2020). Furthermore, the relationship between the amount of carbon stored in aboveground vegetation and soil in AFS could only be evidenced for the topsoil layer [< 10 cm; Ma et al. (2020)]. In Timor-Leste, a large proportion of the population depends on agriculture for their livelihoods. However, population growth, increasing pressure on natural resources, and an increasingly variable climate with an unreliable rainfall regime are threatening food security (World-Bank and Region 2009; Molyneux et al. 2012). In this context, productive, resilient, and sustainable agriculture is critical for the country, and agroforestry has been presented as a potential solution to many of the challenges facing Timorese agriculture (Paudel et al. 2022; Cogné and Lescuyer 2024). Although trees already form a large part of traditional agricultural systems in Timor-Leste (Paudel et al. 2022; Cogné and Lescuyer 2024) and can be key elements for biodiversity conservation (Torquebiau 1992; Perfecto and Vandermeer 2008), they have received little attention and, to our knowledge, no study has quantified carbon storage in traditional Timorese AFS. A precise quantification of these carbon stocks would improve understanding of the ecological importance of these AFS at both local and global scales, and help integrate them into national and international climate change mitigation initiatives. In this study, we aim to better understand traditional AFS in Timor-Leste by quantifying the carbon storage in both biomass and soil of five traditional AFS: cropping systems with fallow, silvopastures, young agroforests, home gardens, and forest gardens. We hypothesize that there are measurable differences in aboveground carbon (AGC; H1a) and SOC (H1b) stocks between these different types of traditional AFS. In line with Ma et al. (2020), we anticipate that there is a positive relationship between AGC and SOC stocks in the topsoil layer (0–10 cm; H2a), but no relationship with SOC below 10 cm (H2b). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses agricultural practices and carbon storage in agroforestry systems, rather than the capacity of human memory. There is no mention or implication regarding memory or cognitive processes.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: A second aim of the experiment related to the effect of semantic relatedness on the quality of the newly learned semantic representations. 1, this analysis had not been possible due to the low number of unrelated paragraphs for which any information was recalled. By extending the learning period, it was expected that performance would be sufficiently improved that we could explore this important effect in more detail. The third aim was to use an additional lexical decision task to assess whether the new meanings had become sufficiently well learned that they could impact on an online speeded task in which there was no requirement for the new meaning to play a role. Experiment 1 had shown that participants are able to retain some information about new word meanings relatively rapidly, but the explicit memory task that was used could not reveal whether this information had become integrated with existing lexical knowledge about the word’s primary meaning. From previous studies of semantic ambiguity (Beretta et al. 2005; Klepousniotou et al. 2008; Rodd et al. 2002), if lexical integration has occurred, then we should see a benefit for the words whose new meaning was semantically related to the existing meaning, as compared with words with an unrelated new meaning. In contrast, if no such integration has occurred, then participants’ speeded responses to these highly familiar words would be unaffected by the semantic-relatedness variable. MethodParticipantsThe 15 participants (six female, nine male; 19–32 years of age) were members of the and were monolingual native English speakers with normal or corrected-to-normal vision. They were paid for their participation. Stimuli and designThe target words, paragraphs, and assignment of items to versions were the same as in Experiment 1. The paragraphs were included in printed booklets that could be taken home by the participants. Each page of the booklet contained just one paragraph. For each version of the booklet, two presentation orders were created. The four possible combinations of version and presentation order were each seen by either three or four participants. The first version was randomly generated, and the second was created by reversing the first. Six rating booklets were created, each referring to one of the following properties: novelty, plausibility, clarity, coherence, logicality, and interest. As with Experiment 1, ratings for each word or paragraph were made on a 7-point scale from low to high. [SEP] Justify:

Output: The text corroborates the claim by discussing the effects of semantic relatedness on newly learned semantic representations and how lexical integration occurs when new meanings are related to existing meanings. This indicates that semantic memory is significant in understanding and retaining word meanings, as it explores the relationship between new and existing meanings, which is essential for comprehension.


Input: Task: contrast the claim using the given text. Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: Second, mismatches between data and methods, data contains categorical (e. g. , country), numerical (e. g. , age), or both types, which have natural constraints on analysis. For categorical values, only the number of items and the mode are statistically relevant6 so that a numerical-orientated method is inherently inadequate. For numerical values, they can be transformed into categorical ones by discretization7, which have been widely applied to knowledge discovery and data mining (KDD) applications8. However, bias occurs if categories are unrepresentative of numerical values. Third, big data challenge, the complexity of data is determined by the number of rows and features (columns). Particularly, computation tasks increase rapidly with the number of features, as known curse of dimensionality (CoD)9. To cope with CoD, dimension reduction and feature selection methods reduce the complexity by extracting information that is practical for classification and cluster analysis. The extraction, a trade-off between efficiency and effectiveness, may involve pruning large amounts of data. Actually, such a pruning manner may have pitfalls10 or miss clues to errors. ResultsWe conducted experiments with two datasets: Wisconsin (Original) (BCWO) and Choice (CMC) 5. Figure 2 (A)(B) and (C)(D) show the results of BCWO and CMC respectively. 2 (A), TC achieves perfect recall (i. e. , recall=1. 0) at the lowest ratio (i. e. , 1:9) and 7 other ratios. It means TC is not only accurate in small amounts of data but also stable when data increases. TC has one error at the ratios 2:8 and 3:7 because the positive observation PO223 is predicted as a negative one. Since the ratio 4:6, PO223 belongs to training data and is not used in testing. For further exploration, at the ratio 10:10#, PO223 is irrelevant to PPP except itself. Indeed, PO223 is related to PP that are also relevant to NO. PPP can eliminate impingement of PO223 on other observations. Novel observations are regarded as positive ones if they are unrelated to both PPP and PNP. Some of NO are predicted as positive so that TC gets lower performance in terms of PR. With increased training observations, TC may obtain sufficient PP or NP to provide accurate results and their causes, e. [SEP] Justify:

Output: The text contrasts the claim by discussing various data challenges and limitations in prediction accuracy. It highlights issues like 'mismatches between data and methods,' the inadequacy of numerical-oriented methods for categorical data, and the complications introduced by the curse of dimensionality (CoD). These factors suggest that predictions may not be perfect—contradicting the claim that AI can achieve perfect accuracy through data analysis.