Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: Materials and methods Study area The research was carried out in the center of the Baucau municipality, located on the north-eastern coast of Timor-Leste. Two communities, or suco (a group of villages), were selected to represent two contrasting topo-geographical, climatic and historical cases. The first, Gariuai suco (long-lat coordinates: 126. 39, -8. 55), is located on a plateau of Baucau limestone, with an annual rainfall of 1757 mm and an altitude ranging between 290 and 740 m above sea level (asl). The second, the village of Osso-Luga in Samalari suco (long-lat coordinates: 126. 49, -8. 57), is located on the Matebian foothills based on Clay, with an annual rainfall of 1343 mm and an altitude ranging between 90 and 260 m asl (Fig. 1). The soils of the Baucau municipality are classified using the WRB soil classification (Mantel et al. 2023) as Lithosols and (2012). The average soil properties differ between the two sites (Table S1); Gariuai has a clay loam texture whereas Osso-Luga has a clay texture. The two villages also have different recent histories. Gariuai has been inhabited for at least 50 years, while Osso-Luga was abandoned during the Indonesian occupation and was only repopulated after Timor-Leste gained independence in 2002. The AFS in Osso-Luga lay fallow during the period of abandonment. 2. 2 Typology of agroforestry systems We used the typology described in Cogné and Lescuyer (2024), which was developed in the same study area. This typology was based on the one proposed by Nair et al. (1993, 2021) and adapted to the local context with semi-structured interviews conducted in the same study area (Cogné and Lescuyer 2024). The five AFS identified were as follows: Crop systems including a wooded Fallow phase (CF), Silvopastures (SP), (YA), (HG), and (FG). CF are plantations of a few non-perennial crops (such as maize, sweet potatoes, groundnuts, and squash) which are left fallow after one to three years. [SEP] Justify:

Output: The text is irrelevant to the claim about human memory as it discusses a research study in the Baucau municipality, focusing on geographical, climatic, and historical aspects of two communities, along with agricultural systems. There is no mention or discussion of human memory or its capacity.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: 01, p >. 9; F 2(1, 33) = 0. 001, p >. 9] or on error rates [F 1(1, 12) = 1. 1, p >. 3; F 2(1, 33) = 1. 2, p >. 2]. Cued-recall testResponses were coded as in Experiment 1. Two different measures of memory performance were used as dependent measures: (1) proportions of correct responses (as in Exp. 1) and (2) the numbers of properties that were recalled for these correct responses. Overall, performance was better than in Experiment 1 (see Table 3), but it remained better for the related items than for the unrelated items in terms on the proportions of correct responses [F 1(1, 13) = 52. 3, p <. 001; F 2(1, 34) = 200. 9, p <. 001] and the numbers of properties that were recalled for these correct responsesFootnote 2 [F 1(1, 12) = 42, p <. 001; F 2(1, 34) = 52. 5, p <. 001]. To explore in more detail this increase in the number of properties that were recalled, the distributions of these individual responses are shown in Fig. 2. Fig. 2Cued-recall results (Exps. 2 and 3): Distribution of the numbers of properties that were correctly recalled on each trial (as percentages of the total number of trials). Open symbols show the mean numbers of properties recalled for the correct trialsFull size image DiscussionIn this experiment, the learning phase was extended across 6 days and participants were tested approximately 24 h after the final learning session, allowing us to examine longer-term memory for novel meanings. Although performance did improve relative to Experiment 1, performance on the unrelated meanings remained relatively poor. For the unrelated paragraphs, participants were only able to correctly recall some information about the word’s new meaning on 52 % of the trials, as compared with 100 % for the related meanings. The amount of semantic information that participants could recall was also significantly lower for the unrelated meanings: For those trials on which some information was correctly recalled, participants recalled an average of 1. 8 properties for the unrelated meanings, as compared with 2. 6 properties for the related meanings. [SEP] Justify:

Output: The text corroborates the claim by demonstrating that participants performed significantly better at recalling properties of related meanings compared to unrelated meanings. This suggests that semantic memory plays a key role in understanding the meanings of words, as evidenced by the reported performance metrics, where correlating meanings yielded higher recall accuracy (100%) versus lower accuracy (52%) for unrelated 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: g. , 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, although AI can identify patterns and make predictions, errors and inconsistencies are present in the data analysis. For instance, it indicates that at various ratios, error rates and insufficient training data lead to extremes, stating that the profile data is 'incapable of explaining behavior' and revealing that '33% of cases are inconsistent inherently'. This suggests that predictions made by AI cannot be considered perfect.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: SP are vast areas (generally 200 to 500 ha) where cattle are grazed under the shade of trees. YA are agricultural plots where farmers plant rows of trees spaced approximately 5 x 10 m apart, which they maintain for two to ten years. HG are plots of land located close to houses where crops and livestock (poultry, pigs, cattle, etc. ) are kept in the shade of numerous trees and palms for multiple uses (wood, fruit, fiber, etc. ). FG are generally former HG (over 50 years old) which have been abandoned, for example as a result of the war, and are now used mainly for gathering with limited human influence. The AFS are presented in more detail in the supplemental information section (Table S2) and in Cogné and Lescuyer (2024). Field inventories We randomly selected three plots from each of the five traditional AFS identified in each of the two sites (30 plots in total; Fig. 1). Sampling plots were defined as the entire agricultural plot (area between 0. 06 and 1. 25 ha) for all AFS except SP plots, which were generally much larger than 1 ha. In SP plots, a 1 ha sampling plot was delineated within the agricultural plot. Tree and soil measurements (described below) were carried out between June and August 2021. Tree measurements Within each sampling plot, all trees with a circumference at breast height (CBH) greater than or equal to 100 cm were identified taxonomically and their CBH and height were measured. Five subplots of 10x10 m (0. 01 ha) were established at each corner and in the center of the sampling plot (Figure S1). Within these subplots, all trees with a CBH between 30 and 100 cm were taxonomically identified and their CBH and height were measured. Height measurements were taken with a dendrometer. Taxonomic identification was carried out using Tetum (a national official language) vernacular names, which were then linked to scientific names using the iNaturalist and Pl@ntNet applications, Geoffrey Hull’s monograph (Hull 2006), the “Useful tropical plants” database (Fern 2014) as well as the Territory Government’s Herbarium (Government, Australia 2013). The correspondence between Tetum names and scientific names is provided in the supplemental material (Table S3). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses agricultural plots and tree measurements, without mentioning 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: Each worksheet was preceded by clear instructions, and we made clear that the participants should only read the paragraphs when directed to by the instructions. 5, the participants returned to the lab and completed the lexical decision task, the vocabulary test, and the explicit memory test. The procedure for these was the same as in Experiment 2. The participants were aware that their memory for the meanings would be tested. ResultsWorksheetsPerformance for the related and unrelated meanings is summarized in Table 5. 1, this score reflects the number of items that were correctly linked to their new meaning. 2 and 4, the scores reflect the numbers of items that were used appropriately within the new sentence/paragraph context. 3, the scores reflect the percentages of questions that were answered correctly. 2–4, the answers were considered correct if they matched the intended meaning in any way, even if they were lacking detail. 2 and 4, the participants were allowed free access to the booklets, and so would be expected to be at ceiling. No effect of Relatedness was expected on these worksheets. 1 and 3, the participants were encouraged to first try the task without the booklet, and so, on the basis of the results of Experiments 1 and 2, we predicted better performance for the related items. Note that these results come from worksheets that were completed outside of the lab, so we cannot be certain that all participants followed the rules exactly. Table 5 Experiment 3: Worksheet results (percentages correct)Full size table Repeated measures ANOVAs were performed on the percentage correct data for each worksheet, averaged across both items and participants. was entered as a within-subjects factor, and Version was entered as a between-subjects factor. 1, participants performed significantly better for semantically related than for unrelated words [F 1(1, 34) = 14. 953, p <. 001; F 2(1, 14) = 15. 283, p <. 01]. In contrast, there were no effects of Relatedness on Worksheet 2 [F 1(1, 34) = 2. 776, p >. 05; F 2(1, 14) = 0. 855, p >. 05], Worksheet 3 [F 1(1, 34) = 0. 008, p >. 05; F 2(1, 14) = 0. 027, p >. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that participants performed significantly better for semantically related words than unrelated words, suggesting the importance of semantic memory in understanding meanings. The specific mention of superior performance for semantically related items indicates that understanding word meanings is facilitated by semantic connections, supporting 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: 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 illustrating that while the proposed method for transparent classification (TC) aims for accuracy, it explicitly addresses the presence of inaccuracies and faults in labeling, suggesting that perfect accuracy is not achievable. It discusses how TC corrects errors in class labels and handles uncertainty without random parameters, emphasizing that there are inherent limitations and complexity in data prediction rather than a claim of perfect accuracy.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: Soil measurements In each of the 30 sample plots, a composite of three samples was taken from three of the five 10 x 10 m subplots, following a transect covering the intra-plot variability and at a minimum distance of 1 m from any tree (Figure S1). Soils were sampled with a 15 cm auger at 0–10 cm and 20–30 cm depth; due to the size of the auger we could not accurately sample the intermediate soil layer. Soils samples were then air-dried and sieved at 2 mm. They underwent standard physical and chemical analyses at Cirad laboratories in Montpellier, France (Table S1). Total organic carbon was determined by dry combustion and measured with a thermal conductive detector. In addition, at one location at the center of the plot, soils were sampled with a cylinder of 250 cm3 at depths of 2–7 cm, 12–17 cm and 22–27 cm. Each sample was dried at 105°C in a classic oven until a stable dry mass was reached and then sieved to 2 mm. The fine fraction (< 2 mm) was weighed to be used in the SOC stocks calculation (Eq. (2. 1)). Carbon stocks estimation Biomass estimation We estimated tree-level AGC stocks from the tree CBH and height measurements using the pantropical allometric equation from Chave et al. (2014) and the R package (Réjou-Méchain et al. 2017). Each tree was assigned a mean and standard deviation of wood density at the species or genus level when at least one value was available from the (Zanne et al. 2009). Other taxa were assigned a plot-level average wood density. The biomass of the palms was estimated by multiplying their estimated volume by their estimated wood density. The volume of the palm trunks was estimated as a cylinder, using the height and diameter measured in situ, with a volumetric shrinkage of 11% (Meier 2007). The height of the palms was measured above the leaves and was therefore multiplied by a conversion factor of 0. 9 to obtain the trunk height. Biomass was then converted into carbon with a factor of 0. 4713 (Thomas and Martin 2012). Belowground carbon (BGC) stocks were estimated at tree level using the root-to-shoot ratio equation from Ledo et al. [SEP] Justify:

Output: The text does not relate to the claim about human memory and its capacity for storing information. Instead, it provides detailed information on soil measurements, sampling methods, carbon stocks, and biomass estimation in ecological research, which is entirely unrelated to the concept of human memory.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: Thus, for those word meanings that were recalled, participants were able to retrieve the same number of new semantic features, irrespective of whether these were for a word that was related or unrelated to the original meaning. General discussionThese three experiments explored how adults learn new meanings for words that are already well established within their lexicon. Given the ubiquity of ambiguity created by acquiring new meanings for previously unambiguous words, this is an important, and yet poorly understood, aspect of adult language learning. The experiments assessed participants’ ability to learn fictitious novel meanings for words with a single dominant preexisting meaning. Two measures of learning were used: a cued-recall test assessing their explicit knowledge of the meanings, and a lexical decision task assessing the impact of this knowledge on online processing. We will discuss the results for these two tasks in turn. The cued-recall task showed that recall of the new meanings was significantly better when these were semantically related to the word’s preexisting meaning. In all three experiments, participants were able to correctly recall information for a higher proportion of the related meanings than for the unrelated meanings. This relatedness effect was particularly marked in Experiment 1, in which participants received only brief exposures to new meanings during incidental learning conditions and were unaware that memory would subsequently be tested. However, the relatedness effect persisted when the training phase was extended to 7 days (Exp. 2) and when semantically demanding tasks were used during training (Exp. 3). In these two experiments, the participants were aware that their memory would be tested, so we cannot be certain about the degree to which this learning reflects either incidental or intentional learning. The most likely explanation for this pervasive relatedness effect is that the semantic relationship between the old and new meanings makes it easier for participants to learn the mapping between form and meaning. Under this view, participants’ primary difficulty for the unrelated meanings is not in acquiring the new meanings themselves, but in knowing which word forms they belong to. Framed in terms of connectionist models of how ambiguous words are processed (Borowsky and Masson, 1996; Joordens and Besner, 1994; Kawamoto, Farrar, & Kello, 1994; Rodd et al. 2004), the connections between form and meaning are more easily learned for the related meanings. [SEP] Justify:

Output: The text corroborates the claim by discussing the importance of semantic relationships in recalling word meanings. It highlights that participants were better at recalling new meanings when they were semantically related to preexisting meanings, indicating that semantic memory plays a critical role in understanding and learning new word meanings. The mention of a 'relatedness effect' supports the assertion that semantic memory is essential for processing and comprehending words.


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: Original PaperSiegel1, MPH, PhD Wiseman2, MPH, PhD Alex Budenz1, MA, DrPH Yvonne Prutzman1, MPH, PhD 1Instiute, of 2University of of :of of MedicinePO Box 800717Charlottesville, VA, 22908United StatesPhone: 1 4342438126Email: kara. wiseman@virginia. eduAbstractBackground: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness. Objective: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors). Methods: Data came from an experiment (ClinicalTrials. gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the Institute’s quitSTART app. Participants’ (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants’ probability of cessation from 28 variables reflecting participants’ use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals’ SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. Results: The SML model’s sensitivity (0. 67) and specificity (0. 67) in the held-aside test set indicated that individuals’ patterns of using different app features predicted cessation with reasonable accuracy. [SEP] Justify:

Output: The text contrasts the claim by discussing the use of supervised machine learning (SML) algorithms to predict smoking cessation success. While it mentions that the model's sensitivity and specificity indicate the patterns can predict cessation with 'reasonable accuracy,' it does not support the idea of perfect accuracy. This implies a limitation in predictive capability, directly opposing the claim of perfect prediction.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: This more limited role for trees may explain why their total biomass is lower in these systems. Indeed, in CF systems, the plot is periodically cleared and the remaining trees are pruned to allow the planted annual crops to receive full sun, while in SP systems, the growth of grazing grasses is favored by reducing the number of trees. It is also interesting to note that, in line with previous results, the age of the trees in the AFS largely explains the carbon stored in their biomass (Ma et al. 2020). The AFS described in this study reflect different stages of transition in land use, which follow the growth of their trees (Figure S2). For example, a farmer might plant trees in a CF field, which will then become a YA that can still produce annual crops for the first 2 to 4 years, before the shade from the trees becomes too great. The trees are then used as property markers and, as they mature, produce wood and fruits; the YA then becomes an HG or FG. Similarly, our results show that AGC stocks increase from CF to YA, HG and FG (Table 1). The location of AFS in the landscape is not random, and could also explain the observed differences in AGC stocks. For example, most HG are located close to houses, in alluvial valleys that are well supplied with water and where soils are enriched with organic waste and animal manure (Palm et al. 2001). The same applies to FG, which are former HG that have been more or less abandoned, and YA, where the farmer has chosen the site with the intention of turning it into a HG. We therefore expect HG, FG and YA to be located in areas that are more favorable for tree growth (Wagner et al. 2012; Madejón et al. 2016; Treuer et al. 2018). Conversely, CF and SP are mainly located on limestone plateaus where water tables are deeper and soils are shallower, which could reduce tree growth and biomass (Imada et al. 2008). Historical and environmental factors explain differences in SOC stocks We found no significant difference in SOC between the AFS (H1b rejected). This result is inconsistent with our expectation that agroforestry practices affect SOC stocks (Minasny et al. 2017; Wiesmeier et al. 2019), and with previous studies that show significant differences in SOC stocks between AFS (e. g. [SEP] Justify:

Output: The text does not relate to the claim about human memory and its capacity to store information. It primarily discusses agroforestry systems (AFS), tree growth, biomass, and various environmental factors influencing these systems. There is no mention of human memory, cognitive capacity, or any related concepts.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: An alternative locus for the relatedness effect on cued recall, which we cannot currently rule out, is that competition between the newly acquired meanings and the existing dominant meaning is increased at the time of recall for words with unrelated meanings. While the effects of relatedness on the recognition memory test used during training (Exp. 3, Worksheet 1) and the lexical decision task (Exp. 3) indicate that relatedness effects are not exclusively limited to tasks with an explicit recall component, further experiments will be needed in order to determine the extent to which meaning relatedness might separately influence the learning and recall of novel meanings. In addition to the explicit test of participants’ knowledge of the word meanings, a lexical decision task assessed whether the newly acquired meanings could affect online comprehension. 2 and 3, participants were faster and less error-prone for the words that they had encountered during training, which likely reflects form-based repetition priming (e. g. , Perea and Lupker, 2004). More interestingly, Experiment 3 showed a significant benefit for words whose novel meaning was semantically related to their preexisting meanings (as compared with unrelated meanings). This finding has important implications for the debate within the ambiguity-processing literature about whether and how meaning relatedness affects lexical decision performance. While several authors have reported that responses to ambiguous words with unrelated meanings are slowed relative to related meanings (Azuma and Van Orden, 1997; Beretta et al. 2005; Klepousniotou et al. 2008; Rodd, 2004; Rodd et al. 2002), other authors have reported no effect of semantic relatedness (e. g. , Hino et al. 2010). These inconsistent findings might suggest that previous effects of meaning relatedness could have arisen from some other, potentially confounding difference between the particular sets of words used in the earlier experiments. Laboratory word-learning paradigms provide a useful method for ruling out alternative explanations of between-item differences on response times (e. g. , for studying lexical competition; cf. Bowers et al. 2005, and Gaskell and Dumay, 2003). In the present work, we showed a significant effect of semantic relatedness on lexical decision responses using a within-item design in which the same sets of words were used (across participants) as both related and unrelated items. [SEP] Justify:

Output: The text corroborates the claim by detailing how the effects of relatedness, derived from semantic memory, influence both the learning and recall of novel meanings. It highlights that words whose novel meanings are semantically related to their preexisting meanings show significant benefits in lexical decision tasks, suggesting that semantic memory plays a crucial role 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: The likelihood ratio test showed that the logistic regression, which included the SML model–predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=. 16). Conclusions: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps. Trial Registration: ClinicalTrials. gov NCT04623736; apps; machine learning; artificial intelligence; smoking cessation; mHealth; mobile health; app; apps; applications; application feature; features; smoking; smoke; smoker; smokers; cessation; quit; quitting; algorithm; algorithms; mobile phone IntroductionCigarette smoking remains a leading cause of preventable death in the [1]. Evidence-based smoking cessation interventions, though proven to be valuable in helping people quit, are underused [2]. Smartphone apps have the potential to expand the reach and increase the use of evidence-based smoking cessation interventions [1,3]. Smartphone ownership is high among every demographic group in the [4], and an array of smoking cessation apps, including many free options [5], are available in smartphone app stores. Evidence suggests that smoking cessation apps are widely used, with millions of downloads per year [6,7]. Research supporting the use of apps for smoking cessation is still emerging, and many publicly available apps have not been rigorously tested [8]. However, results from randomized controlled trials (RCTs) suggest that apps can be effective in helping people quit smoking [9-11]. Studies have also demonstrated that both higher user engagement in smoking cessation apps [11,12] and longer duration and greater frequency of app use [6] are related to smoking cessation. The many capabilities, features, and functionalities that can be incorporated into smoking cessation apps have the potential to increase their effectiveness. Apps can include interactive and multimedia content, and offer tailored features to meet the needs and p2021. The quitwas conducted to test the effects of incentivizing EMA completion within the quitSTART app on short-term smoking cessation. Participants were English-speaking adults who lived in the United States, smoked cigarettes, and had a self-reported desire to quit smoking. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and contextual application of AI and machine learning in developing smoking cessation apps. It emphasizes that while user data can inform the effectiveness of these apps, there is no mention of AI achieving perfect accuracy in predictions. Additionally, the text focuses on empirical evidence and trial results, suggesting a more nuanced and uncertain relationship between data analysis and outcomes rather than a flawless predictive capability.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: , Ramesh et al. 2015). However, it is worth noting that some single-site studies (Gama-Rodrigues et al. 2010; e. g. , Betemariyam et al. 2020) and at least two meta-analyses failed to find any sizable effect of AFS on SOC stocks. Feliciano et al. (2018) highlighted that only 3. 2% of the variance in soil carbon sequestration was explained by the type of AFS, and Shi et al. (2018) found no significant differences in SOC stocks between four different AFS in 354 previous studies. We propose three main explanations to reconcile these different findings. First, in a global meta-analysis, Ma et al. (2020) estimated that it takes only five years for SOC stocks in tropical AFS to reach an equilibrium value. In our study, at least three of the five AFS (namely YA, HG and FG) represent different stages of an AFS transition depending on the age of the trees (Figure S2). Most of these AFS are more than five years old and may have already reached their equilibrium SOC stocks, which could explain why there was little difference between them. Second, we had a limited number of replicates of each AFS within each site, which certainly reduced the power of the statistical tests. We were only able to identify trends, such as higher SOC in SP in Gariuai, which were similar to previous studies (Feliciano et al. 2018). Increasing the number of replicates for each AFS could have increased the power of the tests, and potentially resulted in more significant differences between AFS. Third, other pedoclimatic and historical factors can influence SOC stocks, sometimes to a greater extent than the type of AFS, e. g. , within-site variability in climate, soil texture (Figures S3, S4) or previous land use may have significantly influenced SOC stocks (Wiesmeier et al. 2019; Ma et al. 2020). Our experimental design aimed to limit as much as possible the influence of external co-factors at each site (e. g. , variability in farmers’ management practices, micro-topography, etc. ), but much of the variability cannot be controlled in farmers’ plots. [SEP] Justify:

Output: The text is irrelevant to the claim about human memory, as it discusses soil carbon sequestration and factors affecting agroforestry systems rather than anything related to human memory or its capacity.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: This finding therefore provides converging evidence that meaning relatedness plays a critical role in determining how ambiguous words are represented and processed. What mechanism(s) underlie the semantic-relatedness effect in the lexical decision component of Experiment 3? First, we must rule out the possibility that this relatedness effect reflects a difference in how well these two types of words are learned. A subset analysis of Experiment 3 that only included those items for which an individual participant had learned something about the new meaning (as measured by the cued-recall task) showed a significant effect of relatedness on the lexical decision task. Importantly, for these “correctly learned” items, the results of the cued-recall task showed that participants could recall equivalent numbers of semantic properties for these two sets of items. This indicates that the semantic-relatedness effect on lexical decision performance is present even for items that are equally well learned. Instead, we propose that the relatedness effect reflects the same underlying mechanisms as the meaning relatedness effects seen for well-established ambiguous words (Beretta et al. 2005; Klepousniotou et al. 2008; Rodd, 2004; Rodd et al. 2002). These effects have been modelled within a distributed connectionist model (Rodd et al. 2004) in which recognition of words with multiple unrelated meanings is delayed by the competition between the words’ mutually incompatible semantic representations, whereas recognition of words with multiple related senses is facilitated by their rich semantic representations. The present experiments cannot conclusively determine the extent to which the relatedness effect represents either facilitation from the related meaning or interference from the unrelated meaning. Future studies that include a set of baseline, unambiguous words that are presented along with their existing meanings during training may address this issue, provided that the studies ensure equal exposure to these existing meanings and to the meanings to be learned. For now, we suggest that the relatedness effect observed here reflects some combination of these two effects. Regardless of the precise mechanisms of the relatedness effect, this result indicates that after 5 days of training using a range of semantically demanding tasks, the new meanings have become sufficiently well integrated into participants’ lexicons that they can influence online recognition of these words. Interestingly, a comparable relatedness effect was not seen in the lexical decision component of Experiment 2, which used a similar length of training (7 days) but relatively superficial rating tasks during training. [SEP] Justify:

Output: The text supports the claim by discussing the critical role of meaning relatedness in word representation and processing. It explains how semantic properties can influence lexical decision performance, indicating that after training, new meanings have become well integrated into participants' lexicons, which is essential for 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: Aim 1 AnalysisTo identify which patterns of use of app feature use predict short-term smoking cessation, we built SML models predicting 7-day smoking abstinence from a set of predictor variables that included our binary app feature use variables, our proportion app feature use variables, participants’ total number of app use sessions, phone type (iPhone or Android), and study arm. Phone type was included as a variable in the SML models because the iPhone and Android versions of the quitSTART app were built separately and user data from each app were recorded in a slightly different manner. Although the 2 apps appeared identical to users and we harmonized the user data collected from each, we chose to include phone type as a variable in the SML models in case there was a relationship between phone type and app use or between phone type and cessation. We first randomly divided our data into a training set (n=100, 75% of the data) and a held-aside test set (n=33, 25% of the data). Working with the training set, we used recursive feature elimination with 10-fold cross-validation to determine the optimal number of features for our classifier and then fit our logistic regression classifier using this number of features to the training set. We selected a logistic regression classifier because our outcome variable was binary, and we wanted a classifier that would yield predicted probabilities (rather than binary predictions) for every participant. We evaluated the SML model’s performance in the training set by looking at its sensitivity, specificity, and accuracy. We also examined variable importance (defined as the scaled absolute value of the coefficient of each variable in a logistic regression model for binary classification) for each feature and identified the features in the model assigned the highest importance for predicting cessation. We produced partial dependence plots for each of the top 10 most important features in order to better understand each feature’s relationship with short-term smoking cessation [29]. We then applied the model to the held-aside test set and looked at its sensitivity, specificity, and accuracy. We then used it to produce predicted probabilities of short-term cessation for each participant included in the test set. Aim 2 AnalysisAs a first step toward testing whether participants’ patterns of app feature use predicted unique variance in cessation, we fit 2 logistic regression models using the test set data. [SEP] Justify:

Output: The text contrasts the claim by outlining a methodology where the AI (using SML models) predicts short-term smoking cessation based on data analysis. However, it does not suggest that these predictions are made with perfect accuracy, focusing instead on evaluating model performance metrics such as sensitivity, specificity, and accuracy, which implies variability in predictions.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: We found a significant difference in SOC between the two sites, with higher SOC stocks at Gariuai than at Osso-Luga. A priori, three factors that differentiate the sites could explain these differences in SOC stocks: climate (through elevation; Fig. 1; Figure S3), soil texture (Table S1) and history. First, we found that SOC stocks were positively correlated with elevation, both between and within sites (Figure S3). This result is consistent with those of Raich et al. (2006), who found higher SOC at higher elevations in tropical evergreen forests in various countries (e. g. , Congo, Thailand). As changes in elevation are associated with variations in climate (precipitation, temperature), our result is also consistent with previous studies that found higher SOC in areas with higher precipitation (Schuur et al. 2001) and lower temperature (Raich et al. 2006; Marín-Spiotta and Sharma 2013). Second, soil texture is a key driving process for SOC storage in the tropics (Don et al. 2011; Wiesmeier et al. 2019; Matus 2021). In particular, many studies have found a strong positive correlation between SOC stocks and the fine mineral fractions of soils [i. e. , clay or clay + fine silt content; Zinn et al. (2005)]. In our study, we found no significant correlation between SOC stocks and clay + fine silt content when site and depth were included in the model (Figure S4). Furthermore, the Gariuai soils, which had the highest SOC stocks, were also the least clayey. This contradicts the typical (positive) relationship between the fine mineral fraction and SOC stocks. It is therefore unlikely that the difference in SOC stocks between the two sites can be attributed to soil texture. Thirdly, history could be a significant driving factor. Gariuai was continuously inhabited for over 50 years, whereas Osso-Luga was abandoned for almost three decades. The management and care of the AFS in Gariuai, such as pruning, organic inputs related to the presence of livestock, as well as optimization of the different strata in terms of C input, may have led to an increase in soil organic carbon compared to abandoned systems (Lorenz and Lal 2014). [SEP] Justify:

Output: The text is irrelevant to the claim regarding human memory, as it discusses factors affecting soil organic carbon (SOC) stocks, including climate, soil texture, and historical management, without any mention of memory or information storage.


Input: Task: corroborate the claim using the given text. Claim: Semantic memory is crucial for understanding meanings of words. [SEP] Text: We therefore speculate that a high level of semantic engagement may be a necessary requirement for participants to integrate newly acquired meanings with their existing lexical knowledge. It may seem surprising that these newly learned meanings can influence participants’ lexical decision responses to well-established, highly familiar words with a single, strongly dominant meaning (e. g. , “ant” or “path”). Participants have received relatively little exposure to the novel experimental meanings, as compared with their lifetimes’ experience with the preexisting dominant meanings, and reading time experiments have consistently shown that subordinate meanings have minimal impact on processing unless they are strongly supported by the sentence context (e. g. , Duffy, Morris, & Rayner, 1988). The effect is particularly striking given that participants had not encountered the new meanings since the previous day. This finding is consistent with data from recent priming experiments (Rodd et al. 2012) that have shown that participants’ relative p504. doi:10. 1006/jmla. 1997. 2502 459. doi:10. 3758/BF03193014 65. doi:10. 1016/j. cogbrainres. 2004. 12. 006 85. doi:10. 1037/0278-7393. 22. 1. 63 54. doi:10. 1016/j. cognition. 2005. 02. 002 4883. 608. doi:10. 1016/S0022-5371(83)90364-X 3800. doi:10. 1098/rstb. 2009. 0111 446. doi:10. 1016/0749-596X(88)90066-6 39. doi:10. 1111/j. 1467-9280. 2007. 01845. x 613. doi:10. 1080/01690960601017013 132. doi:10. 1016/S0010-0277(03)00070-2 196. doi:10. 1037/a0020475 PubMed 1062. doi:10. 1037/0278-7393. 20. 5. 1051 1247. doi:10. 1037/0096-1523. 20. 6. 1233 1543. doi:10. 1037/a0013012 353. doi:10. 1016/j. cogpsych. 2007. 01. [SEP] Justify:

Output: The text corroborates the claim by highlighting the importance of semantic engagement in integrating newly acquired meanings with existing lexical knowledge. The statement indicates that understanding meanings of words is influenced by both newly learned and preexisting dominant meanings, which connects directly to the idea that semantic memory plays a crucial role in this understanding.


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: As can be seen in Figure 1, this feature was negatively related to the probability of cessation, indicating that users who reported “slipping up” more often, proportional to their app use, were less likely to successfully quit smoking. The second and third most important features in the model, respectively, were nexplorecontentpages_prop and nbadgescompleted_prop. The former variable represents the number of times a participant viewed “Tips,” “FYIs,” or “Inspirations” content pages in the app divided by their number of app use sessions. The latter represents the number of badges a participant earned for reaching milestones in their cessation journey or use of the quitSTART app (eg, checking the app 5 times in 1 day) divided by their number of app use sessions. Both of these variables were positively related to cessation, showing that participants who used these app features more often were more likely to successfully quit smoking. Other features that were among the top 10 with the highest variable importance were naddlocation_prop, ncompletedemas_prop, studyarm, nprogresspressed_prop, noquitdate_bin, and nfeelinggreatpressed_prop. After building our SML model and assessing feature importance in the training set, we fit the model in the test set. The model’s accuracy was 0. 67, and both its sensitivity and specificity were also 0. 67. We retained the SML model–predicted probabilities of cessation as a variable in the test set. Results from the 2 logistic regression models performed in the test set are summarized in Table S1 in Multimedia 1. The likelihood ratio test comparing model 1, which included our set of participant characteristics that are known to be related to cessation, to a null model was not statistically significant at the α=. 05 level (χ29=5. 0; P=. 84). Likewise, the likelihood ratio test comparing model 2, which included all variables included in model 1, as well as the predicted probabilities of cessation from the SML model to a null model was not statistically significant (χ210=7. 0; P=. 73). The likelihood ratio test comparing model 2 to model 1 was not statistically significant (χ21=2. 0; P=. 16), indicating that model 2 provided a statistically equivalent fit to the data to model 1. [SEP] Justify:

Output: The text contrasts the claim by illustrating that the AI model described achieved an accuracy of only 0.67, and its sensitivity and specificity were also 0.67. This indicates that the model does not predict future outcomes with perfect accuracy, as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: SOC stocks are not explained by variation in AGC stocks We found no significant relationship between AGC and SOC stocks in either the topsoil layer 0–10 cm depth (H2a refuted) or at 0–30 cm depth (H2b confirmed). Overall, AGC stocks were a poor predictor of SOC stocks. These results may relate to previous findings that SOC stocks saturate much faster than AGC stocks in tropical AFS. For example, in their global meta-analysis, Ma et al. (2020) estimated that it took only five years for SOC stocks in tropical AFS to reach an equilibrium value, but more than three decades for AGC stocks. This desynchronization of SOC and AGC stocks recovery has also been observed in naturally regenerating tropical forests. In a systematic review, Martin et al. (2013) found that SOC in tropical secondary forests changed little with time after disturbance, while AGC increased. Ojoatre et al. (2024) found similar results in a secondary tropical mountain forest. These results suggest that there is little correlation between AGC and SOC stocks once AFS are more than five years old, which is the case in our study for FG, HG (> 5 years old) and partially for YA (2 to 10 years; Table S2). This saturation of SOC stocks even with increasing AGC stocks is counterintuitive, as it might be expected that higher AGC stocks would lead to more litter production and decomposition, some of which would increase SOC stocks. However, Sayer et al. (2019), found that SOC stocks did not increase in a lowland tropical forest in Panama after 15 years of an experimentally doubled aboveground litter input. Two processes could explain these findings. On the one hand, the addition of easily decomposable litter could stimulate microbial decomposition and turnover of old stored SOC [i. e. , priming effect; Sayer et al. (2019)]. On the other hand, the capacity of the soil to store SOC through organo-mineral associations would be limited by its fine element content (clay + fine silt) (i. e. , saturation concept). In addition to biomass stocks, other vegetation characteristics may account for variations in soil carbon stocks between AFS. For instance, previous studies have shown that tree diversity can enhance SOC sequestration in natural ecosystems (Chen et al. 2018; Spohn et al. [SEP] Justify:

Output: The text is irrelevant to the claim regarding human memory. It discusses the relationship between soil organic carbon (SOC) stocks and aboveground carbon (AGC) stocks in tropically affected agroforestry systems, with no mention of memory or information storage capabilities. The findings and examples provided focus solely on carbon stocks and ecological processes, which do not relate to the concept of human memory.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: between reading comprehension and phonetic and non phonetic reading skills in children with normal hearing and children with cochlear implants Submit manuscript. . . Journal of eISSN: 2379-6359 Otolaryngology-ENT 5. DOI: 10. 15406/joentr. 2023. 15. 00517 primary purpose of this study was to explore the reading comprehension skills and phonetic and non-phonetic reading skills in the English language in both, children with normal hearing and children with cochlear implantation. Research in this area is limited with respect to the Indian context and hence a study dealing with these aspects has the potential to broaden our understanding of benefits of cochlear implantation as it helps with academics. The study involved a total of 74 participants across three grades, 5th, 6th and 7th of which 57 were CWNH and 17 were CWCI. All were assessed based on scores attained on 4 assessment tasks, phonetic reading skills, non-phonetic reading skills, short reading passage comprehension and long reading passage comprehension. On statistical analysis, there was a significant difference between scores obtained on phonetic reading, short reading passage comprehension and long reading passage comprehension between 5th and 6th as well as 6th and 7th graders of CWNH. However, no significant difference was recorded between all three standards (5th, 6th and 7th) on all 4 assessment tasks for CWCI group. When grade level comparisons were made between CWNH and CWCI, there was no statistically significant difference of scores on any of the 4 tasks between the two groups at any grade level except that 6th graders of CWNH group performed better in the short reading passage comprehension task as compared to 6th graders of CWCI. Keywords: cochlear implant, reading comprehension, phonetic words, non-phonetic words Abbreviations CWNH, children with normal hearing; CWCI, children with cochlear implantation single best predictor of reading success has been known to be phonological awareness. That is, children need to be aware of the sounds of speech in order to acquire knowledge of sound-letter correspondence and use this knowledge to decode the printed word. If a child is not aware of the sounds contained in a word, the child will have difficulty associating sounds with letters. [SEP] Justify:

Output: The text corroborates the claim by emphasizing the importance of phonological awareness, stating that 'the single best predictor of reading success has been known to be phonological awareness.' It also explains that children need to be aware of the sounds of speech to decode printed words, indicating that phonetic variations significantly affect reading 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: The variables considered for inclusion in our sensitivity analysis SML model are summarized in Table S2 in Multimedia 1. Recursive feature elimination showed that the optimal number of features to include in the model was 28. The model’s accuracy in the training set was 0. 88, its sensitivity was 0. 94, and its specificity was 0. 71. The most important feature of the model was studyarm, which represented the participants’ assigned study arm. The importance metrics for each feature included in the model are displayed in Figure S3 in Multimedia 1. The model’s accuracy in the test set was 0. 64. Its sensitivity was 0. 75 while its specificity was 0. 33. We fit 2 logistic regression models in the test set (see Table S3 in Multimedia 0. 6; P=. 46), indicating that model 2, which included the predicted probabilities from the SML model using continuous app feature use variables, did not provide a significantly better fit to the data than model 1. Figure 1. Partial dependence plots depicting the predicted marginal effects on the probability of cessation for the 10 app use variables assigned the highest feature importance. The x-axis in each figure is constrained to show only values of each variable that were observed in the training set used to build the supervised machine learning model. DiscussionPrincipal FindingsWe developed and tested a novel approach to using SML to examine whether and how the use of specific features within a smoking cessation app predicts short-term cessation. We applied SML models to data from the quitto identify patterns of app feature use that predict short-term smoking cessation. Our analysis of variable importance within this model indicated that the 3 app feature use variables that were most important for predicting cessation were the number of times participants pressed the “I Slipped” button, the number of times they viewed the “Tips,” “FYIs,” or “Inspirations” content pages, and the number of badges they completed (each expressed as a proportion of total app use sessions). We then used a likelihood ratio test comparing 2 logistic regression models to assess whether including patterns of app feature use in our models allowed us to better predict cessation. [SEP] Justify:

Output: The text contrasts the claim by outlining a specific analysis using a supervised machine learning (SML) model that suggests while patterns can be identified to predict short-term cessation in smoking behavior, the model's accuracy is not perfect; for instance, it achieved an accuracy of only 0.64 in the test set and showed variability in sensitivity and specificity. This indicates that AI cannot predict outcomes with perfect accuracy, as implied by the claim.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: 2023) and in AFS (Islam et al. 2015; Manaye et al. 2021). Moreover, total SOC may be less sensitive to management practices (such as biomass inputs) than specific fractions of SOC (Haynes 2005). Therefore, it would be interesting to continue with further studies that focus not only on the quantity but also the quality of soil organic matter and its relationship with biodiversity (flora and fauna) in traditional Timorese AFS. How to conserve and improve carbon stocks in traditional AFS in Timor-Leste? Although agroforestry can make agriculture more resilient to climate change while improving its carbon footprint and biodiversity conservation value (Cardinael et al. 2021), poor adaptation to the local context can explain many failures to adopt new agroforestry techniques (Coe et al. 2014). Conversely, some traditional agroforestry techniques have evolved in a particular context (biophysical, social and economic) and have adapted to this context (Gouyon et al. 1993; Aumeeruddy 1994; Peltier 1996). This is the case of the traditional AFS studied here in Timor-Leste. Their heritage and socio-economic values (as shown by Cogné and Lescuyer 2024), in addition to their high carbon stock value, confirm the importance of their conservation, both nationally and internationally. These traditional AFS should be conserved to prevent them from being abandoned as the trees wither or their productivity declines. Clear-cutting old agroforests for full-sun crops can appear to be an attractive short-term strategy, providing a large cash inflow from the sale of wood products, followed by four or five years of agricultural production on fertile land. However, this practice unfortunately results in a significant loss of biodiversity and carbon stored in the biomass, and can lead to an irreversible loss of fertility on the plot through soil degradation and erosion. This phenomenon has been documented in a similar context in Sumatra (Indonesia), where Gouyon et al. (1993) highlighted the fact that old agroforests, which they called “jungle-rubber”, and which are quite similar in structure to “forest gardens” (except for the rubber tree component, which has been replaced by other species in Timor-Leste), were being cleared in favor of more intensive production systems. [SEP] Justify:

Output: The text does not address the claim regarding human memory or its capacity to store information. Instead, it discusses topics related to agroforestry, biodiversity, and carbon stock management, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: AbstractStudents acquiring Language (ASL) as a second language (L2) struggle with fingerspelling comprehension more than skilled signers. These L2 learners might be attempting to perceive and comprehend fingerspelling in a way that is different from native signers, which could negatively impact their ability to comprehend fingerspelling. This could be related to improper weighting of cues that skilled signers use to identify fingerspelled utterances. Improper cue-weighting in spoken language learners has been ameliorated through explicit phonetic instruction, but this method of teaching has yet to be applied to learners of a language in a new modality (M2 learners). The present study assesses this prospect. Eighteen university students in their third-semester of ASL were divided into two groups; one received explicit phonetic training, and the other received implicit training on fingerspelling. Data from a fingerspelling comprehension test, with two experimental conditions and a control, were submitted to a mixed effects logistic regression. This revealed a significant improvement from the pre-test to post-test by students who received the explicit training. Results indicate that even short exposure to explicit phonetic instruction significantly improves participants’ ability to understand fingerspelling, suggesting that ASL curricula should include this type of instruction to improve students’ fingerspelling comprehension abilities. I IntroductionFingerspelling is a loanword system for borrowing orthographic representations of English words into Language (ASL). It is very often used for proper names and brand names that do not have conventional lexical signs (Padden, 1998). It can also be used for emphasis or even to borrow the exact semantics of an English word into an ASL sentence where the closest translation equivalent is still slightly different. As with all linguistic systems, fingerspelling has multiple levels of linguistic structure. The phonology of signed languages generally includes specifications for handshape, movement, location, and orientation of the palm (Battison, 1978; Stokoe, Casterline & Croneburg, 1965; Valli, Lucas, Mulrooney, & Rankin, 2011). In fingerspelling, the phonological level – handshape and orientation specifications – consist of abstract units that are contrastive with one another (-a- versus -s-, -m- versus -n-, or -h- versus -u-). [SEP] Justify:

Output: The text corroborates the claim by discussing how explicit phonetic instruction can significantly improve the comprehension abilities of students learning fingerspelling in American Sign Language (ASL). It states that these learners struggle due to improper cue-weighting and that phonetic training has led to measurable improvements in their fingerspelling comprehension, thus highlighting that phonetic variations (in this case, handshape and orientation in ASL) do indeed influence language 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: The results of this likelihood ratio test showed that the logistic regression model that included both the SML-predicted probabilities of cessation based on participants’ app feature use, as well as a set of variables reflecting participants’ baseline tobacco use and demographic and personal characteristics did not fit the data better than a model that included only the latter variables. This means the accuracy of our model predicting whether participants quit smoking was not improved by including the SML-predicted probabilities. However, because only observations from the held-aside test set (n=30) were included in this analysis, the small n likely contributed to this null result. This study adds to the small but growing body of literature that has gone beyond looking at the overall relationship between smoking cessation app use and smoking cessation to examine which specific app features are associated with cessation [20,21]. Some of our findings align with those from prior research. For example, our finding that completing badges is an important variable for predicting smoking cessation aligns with the finding reported by Rajani et al [20] that participants’ frequency of use of gamification features, including earning badges, was associated with motivation to quit. However, there is a need for more research investigating different app features within smoking cessation apps to help maximize the potential public health impacts of smoking cessation apps. The methodological approach developed in this study could be used to guide additional research evaluating smoking cessation apps and to improve the design and refinement of such apps. While this study focused on smoking cessation, this approach could also be applied in research on apps focused on other health behaviors. Our methodological approach could help guide further research in several ways. For example, our finding that patterns of app feature use did not predict unique variance in cessation might lead researchers to explore whether there is variability in the extent to which different groups of app users are helped by different app features. Alternatively, finding that patterns of app feature use did predict unique variance in cessation might inspire additional research investigating users’ perceptions of, satisfaction with, and reasons for using the app feature use variables that were found to be important for predicting cessation. Additionally, if an app feature uses a variable that was expected to be effective based on theory and prior research was not found to be important in predicting cessation, researchers might investigate why this was the case, considering possible explanatory factors such as design and usability issues [15,18]. This research could also inform the design of new apps, as well as the refinement of existing apps. [SEP] Justify:

Output: The text contrasts the claim by explaining that the model's ability to predict smoking cessation was not improved by including AI-predicted probabilities, indicating that the prediction was not accurate as claimed. The specificity of model performance regarding app feature use suggests limitations to predictive accuracy and a need for more research, thereby rebutting the assertion that AI can achieve perfect accuracy in predictions.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: This trend toward clearing jungle-rubber was subsequently confirmed by Ekadinata and Vincent (2011), and its impact on carbon stock decline was quantified by Villamor et al. (2014). Strategies to avoid the clearing of traditional AFS could involve new ways of intensifying production while maintaining high carbon and biodiversity values, and gradually renewing the tree population in the plots. For example, it would be interesting to improve tree regeneration by clearing about one hundred square meters (corresponding to the felling of 1 to 10 trees, depending on their crown area). In these clearings, young trees of species considered interesting for their future production could be associated with first heliophile crops (e. g. , cereals), and then shade-tolerant crops (e. g. , taro). Additional mechanisms to encourage the renewal of traditional AFS could include the provision of subsidies (e. g. , payment of carbon credits) or low-interest loans to farmers wishing to renew their agroforestry plots. Another strategy, which has been successfully implemented in other tropical countries (e. g. , for agroforestry coffee), is to improve the incomes of AFS farmers through ‘sustainable agriculture’ certification (Bertrand et al. 2019), although this is primarily applicable to the production of export goods, whereas the AFS studied here are currently oriented toward local consumption and domestic markets. ConclusionAgroforestry systems in the Baucau municipality of Timor-Leste had variable but overall high carbon stocks. These systems have only recently been described in the scientific literature, and more research is needed to improve our knowledge of their diversity and functioning. It is therefore important that all stakeholders, including local authorities, universities, and NGOs, are aware of their existence, diversity, and environmental and social value, in order to propose possible innovations to maintain or improve their functioning. DeclarationsAuthor ContributionAll authors contributed to the design of the sampling protocol. MC carried out the fieldwork. CP analyzed the data with the help of MC and VF. CP prepared the manuscript (including figures and tables) and all authors contributed to the drafting and revision of the manuscript. AcknowledgementWe are grateful to (Cirad) for his facilitation of the field studies and critical revision of the paper, and to the trainees who helped with field data collection: Santos (vegetation) and (soil). [SEP] Justify:

Output: The text is irrelevant to the claim about human memory storing information. It discusses agroforestry systems, their carbon stocks, and strategies to improve agricultural practices, with no mention or implication of memory capacity or any related concepts.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: a The explicit training program: Experimental groupIn between their pre- and post-tests, participants in this training group received an explicit fingerspelling training. There are two main sections. The first includes information about the structure of fingerspelling. For example, consider the images in Figure 2. These images draw participants’ attention to the differences between hold and transition segments. In hold portions of the signal, the video is clear and little-to-no movement, identified by blurring, is visible. Transition segments do exhibit blurring, and the configuration of the hand shows features of the previous and subsequent letters. This means one could potentially use transition information to predict subsequent letters, leading to faster lexical recognition. 2b the letters -s-, -o-, -r-, and -t- can be identified, while in Figure 2c the first image is slightly blurry and looks to have features of both -s- and -o-, the second with features of -o- and -r-, and the final one with -r- and -t-. These images are described in detail in the explicit training. Figure 2. Example token in still images, extracted from videos, of the word s-o-r-t. The clear video (a) presents participants with unmodified stimuli: all portions of the signal are present. The holds only condition (b) presents stimuli in which the frames of transition are masked, and the transitions only condition (c) provides the opposite: the frames in which a posture is held are masked. The second part of the training focused on describing the types of phonetic variation that students might encounter when they see fingerspelling. Consider the two words in Figure 3. Both include a u-r bigram (two-letter sequence). To make this otherwise hard-to-differentiate letter combination more distinguishable, this signer adds ulnar deviation (sideways flexing of the wrist toward the pinky) and supination (upward rotation) of the forearm, which, taken together, indicate the appearance of -u- and -r-. In both the Figure 3a and Figure 3b examples, neither letter is realized in its canonical form, but this extra movement makes it clear that both letters are intended, as it does not occur with either letter individually, nor with the combination -r- and -u-. Figure 3. [SEP] Justify:

Output: The text corroborates the claim by illustrating how phonetic variations, such as the differences in fingerspelling through transition and hold segments, can enhance language comprehension. Specifically, it mentions that using transition information can lead to faster lexical recognition, indicating that variations in phonetic representation (like those exhibited in fingerspelling) directly impact the understanding of language.


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-3634865/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractToday, it seems natural to discuss or, if you prefer, Machine Learning. Many observers of digital marketing and tourism trends in recent years have not missed an opportunity to emphasize how these technologies will become increasingly prevalent and pervasive in our digital age, especially in the travel and tourism sector. But what are machine learning and deep learning? They are branches of artificial intelligence that provide computers with the ability to ’learn without being explicitly programmed’. In essence, when properly trained, artificial intelligence can respond to novel situations based on reasoning and past experiences. This technology, particularly artificial intelligence in tourism, paves new pathways allowing the automation of tasks that were previously done manually. Even in the hospitality industry, it can be successfully deployed for guest assistance tasks, both before and during their stay. Having an assistant on the site communicating with the visitor in real-time without any delay is an effective way to convert them into a customer. Statistics are confirming a correlation between response time to inquiries and the site’s conversion rate. Moreover, during the stay, the use of chatbots has proven its worth. Having a continuously active assistant ready to respond is undoubtedly a boon for the establishment. It also includes activities where there’s a need to understand the guest’s requirements and make decisions, such as recommending services to the guest or preparing a quote. Fortunately, the future will not solely consist of chatbots and robots. A new era of tourism is on the horizon, where humans will spend less time on repetitive tasks and more time catering to and entertaining guests in ways that, for now, only we humans can. In this study, we utilized data from several hotel reviews. Each review represents a client’s assessment of a hotel. For each textual review, we aim to predict whether it corresponds to a positive review (the customer is satisfied) or a negative review (the customer is dissatisfied). The overall ratings of the reviews can range from 2. 5/10 to 10/10. To simplify the issue, we’ll categorize them as follows: negative reviews have overall ratings of less than 5; positive reviews have ratings of 5 or higher. [SEP] Justify:

Output: The text contrasts the claim by indicating that while AI, specifically in the context of analyzing hotel reviews, aims to predict customer satisfaction levels based on past data, it does not suggest that AI can predict the future with perfect accuracy. Instead, the focus is on the categorization of reviews into positive and negative based on predefined rating thresholds. This implies a level of uncertainty and limitation in AI's predictive capabilities rather than absolute accuracy.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: We would also like to thank the 31 farmers who gave their time to take part in the interviews and field measurements. A first English version of this paper was proofread by Grace Delobel. This project was co-funded by the and the Ministry for and Development (BMZ) as part of the Partnership for (PSAF) - Ai ba Futuru project, implemented by the für (GIZ). Additional funding for this study was provided by the in Jakarta and the of Timor-Leste. Data AvailabilityAll data collected for this study are available in the following private Dataverse repository: R codes used to analyze the data and generate the manuscript (including figures and tables) are provided as supplementary files (manuscript file). Upon acceptance of the 27. (1994) Local representations and management of agroforests on the periphery of (2022) A global overview of studies about land management, land‐use change, and climate change effects on soil organic carbon. Biology 28:1690–1702. Bertrand B, Breitler J-C, Georget F, et al (2019) The agroecological transition of agricultural systems in the Global South. éditions (2020) of in Systems in Ethiopia. Small-scale Forestry 19:319–334. (2020) Above-ground carbon stocks and timber value of old timber plantations, secondary and primary forests in southern Ghana. and Management 472:118236. (2021) Climate change mitigation and adaptation in agriculture: Why agroforestry should be part of the solution. Agriculture, Ecosystems & Environment 319:107555. (2004) Error propagation and scaling for tropical forest biomass estimates. Philosophical transactions of the of B, Biological sciences 359:409–420. Chave J, Réjou-Méchain M, Búrquez A, et al (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Biology 20:3177–3190. (2018) Plant diversity enhances productivity and soil carbon storage. Proceedings of the of Sciences 115:4027–4032. (2014) Scaling up agroforestry requires research ’in’ rather than ’for’ development. in 6:73–77. [SEP] Justify:

Output: The text is irrelevant to the claim regarding human memory. It does not mention memory at all; instead, it discusses acknowledgments for contributions to a research project, funding sources, and references to various studies related to agriculture and climate change. Therefore, it passes as it does not address the claim.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: (a) c-o-u-r-s-e and (b) s-o-u-r-c-e naturally fingerspelled. Figure (a) exhibits both ulnar deviation and supination of the forearm, while (b) exhibits the former only. This movement epenthesis is consistent with Brentari’s (1998) proposed phonological rules governing local lexicalization, or words that become appreciably reduced phonologically but only in a particular discourse. Long, academic terms for which there is no conventional sign are prime candidates for local lexicalization. One likely explanation for the development of epenthetic movement with -u- and -r- but not the reverse, -r- and -u-, is because of bigram frequency; -u- and -r- is the 50th most frequent English bigram, occurring in 0. 54% of words, while -r- and -u- is far less common, occurring in only 0. 128% of English words (Norvig, 2015). Other examples of gross movements to attend to in fingerspelling include epenthetic wrist flexion (bending downward) with the appearance of -y- word internally or word finally, and a blending of features of the letters -g-, -h-, and -t-, pictured in Figure 4. This training lasted approximately 30–40 minutes. Figure 4. Two images of the transition between -g-, -h-, and -t- in the fingerspelled word n-i-g-h-t. Image (a) shows the orientation shift (supination) required by the letters -g- and -h-. Image (b) shows outward rotation (pronation) of the forearm, or returning to the default, palm-out orientation. Both images show the anticipation of the letter -t-, which requires the index and middle finger to be separated, to allow for insertion of the thumb between them. Also note that in (a), the pinky remains partially extended from the previous letter -i-, while in (b) it has returned to a more flexed position. Knowledge of anticipatory and perseveratory coarticulation would help in comprehension of words with this letter combination. b The implicit training program: Control groupStudents in the control, or implicit training group, also received training in fingerspelling. [SEP] Justify:

Output: The text corroborates the claim by discussing how knowledge of anticipatory and perseveratory coarticulation aids in the comprehension of words with specific letter combinations. This indicates that phonetic variations, such as those in fingerspelling, can influence how language is understood.


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: The challenge lies in predicting this information using only the raw textual data of the review. Hospitality and 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 11Figure 12Figure 13Figure 14Figure 151 IntroductionIn the modern era, with the dawn of the digital age, we’re seeing the rise of novel unstructured data. Coupled with the conventional structured data, it has paved the way for the revolution. Concurrently, the progress in computational capabilities and swift advancements in algorithmic designs have given birth to sophisticated analytics, surpassing the usual to extract more profound understandings and forecast future trends. Each technique possesses its unique merits and demerits, tailored to address distinct challenges. By harnessing this rich array of techniques, the tourism sector can discern previously hidden patterns and relationships, perform real-time analysis, and dive into the depths of insights to more comprehensively grasp and elucidate various phenomena, systems, and mechanisms, among others. Notably, the forward-looking capability of certain methodologies enables us to pose and answer questions about the future (Weihs - Ickstadt, 2018). Within the tourism sector, manifests its capabilities through avenues such as travel route enhancement, predictive modelling for trend projections, bespoke experience algorithms, sentiment evaluation and feedback extraction, and real-time alert and surveillance systems, among others. The statistical representation of tourism is particularly challenging, as it pertains to measuring a phenomenon that does not concern a single sector but a set of services, some of which are highly heterogeneous. Tourism can be analyzed from both a demand and supply perspective and is thus measured through various information sources. In general, statistics that look at tourism from the supply side refer to the capacity and occupation of accommodation facilities, while those that measure the tourism phenomenon from the demand side are based on information directly collected from travellers at borders or through traditional household surveys. The tourism domain is intricate and multifaceted, making it a prime arena for the deployment of (AI) solutions that leverage voluminous datasets. Given the multitude of individuals, operations, and activities encapsulated within, data in tourism often falls within the big data category. Furthermore, the potential seems boundless regarding the variety of data origins and AI methodologies that can be harnessed to scrutinize and enhance both the provision and consumption of tourism services, spanning from marketing tactics to over-tourism management. [SEP] Justify:

Output: The text contrasts the claim by discussing the challenges of predicting information accurately using only raw textual data, emphasizing that while certain methodologies can enable projections and discern patterns, the inherent complexity and multifaceted nature of the tourism sector make perfect accuracy unattainable. The acknowledgment of distinct challenges in forecasting indicates that AI, despite its advancements, cannot predict the future with absolute certainty.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: Cogné M, (2024) The role of agroforestry in farmers’ strategies and its contribution to the well-being of rural people in Timor-Leste. and Livelihoods 00:1–26. (2011) Impact of tropical land-use change on soil organic carbon stocks - a meta-analysis. Biology 17:1658–1670. (2021) Agroforestry contributions to smallholder farmer food security in Indonesia. 95:1109–1124. Ekadinata A, (2011) Rubber agroforests in a changing landscape: analysis of land use/cover trajectories in Bungo district, Indonesia. Forests, Trees and Livelihoods 20:3–14. (2012) Harmonized world soil database (version 1. 2). FAO, Rome, Italy and (2018) Which agroforestry options give the greatest soil and above ground carbon benefits in different world regions? Agriculture, Ecosystems and Environment 254:117–129. (2014) Useful tropical plants Gama- (2010) in in Bahia, Brazil. 45:274–283. (2010) Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s. Proceedings of the of Sciences 107:16732–16737. Gouyon A, de, (1993) Does ‘jungle rubber’ deserve its name? An analysis of rubber agroforestry systems in southeast Sumatra. 22:181–206. Harvey CA, Chacón M, Donatti CI, et al (2014) Climate‐Smart Landscapes: Opportunities and Challenges for and Mitigation in Tropical Agriculture. 7:77–90. Haynes RJ (2005) as of the Quality of Agricultural Soils: An Overview. pp 221–268 (2006) Timorese plant names and their origins. de Linguı́stica (2013) Tree height and tropical forest biomass estimation. Biogeosciences 10:8385–8399. (2008) Water table depth affects Populus alba fine root growth and whole plant biomass. 22:1018–1026. (2015) Effect of on in the System of North-Eastern Bangladesh. Small-scale Forestry 14:91–101. (2004) Soil carbon sequestration impacts on global climate change and food security. [SEP] Justify:

Output: The text is irrelevant to the claim about human memory and its capacity to store information. Instead, it discusses various studies and publications regarding agroforestry, land-use changes, and agricultural practices. There is no mention or inference related to human memory in the provided text.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: The first research question asks whether the explicit training will help students improve more on a fingerspelling comprehension measure than those students who received the implicit training. The model reveals that students with explicit training improved significantly from their pre- to post-test by a statistically significant margin (p < 0. 05). The second question asks whether the training, which focuses on drawing students’ attention to information contained in the transition segments through descriptions of phonetic variation, impacts performance in all experimental conditions equally. No such interaction was found, which will be discussed in Section IV. These results can be seen visually in Figure 5. Two important features of this figure to note are: (1) Student performance on the pre-test is roughly equal across groups, and (2) students in the explicit training group improve across conditions on the post-test. This is supported by the statistical analysis detailed above. Figure 5. Model predictions plot. Dots represent model predictions and lines represent 95% confidence intervals. The x-axis shows the two types of test. Within each condition, the pre-test is always on the left and the post-test is always on the right. Conditions are labeled at the top: allClearA has video of the full word with no masking (control condition); holdsOnly and transitionsOnly have masking such that the holds or transitions are the only visible portions of the signal (experimental conditions); and allClearB has the full video – like allClearA – and serves as a second control condition. The y-axis represents the probability of a correct response in each condition, on both tests, and for each group of participants. Values for the implicit group in red, appear on the left, and values for the explicit group in blue appear on the right. IV DiscussionThe goal of this study was to determine whether explicit phonetic instruction would benefit adult students acquiring ASL as a second language, with a particular focus on fingerspelling comprehension. Specifically, this study assesses whether knowledge about the structure of fingerspelling – and the ways in which ASL phonology (abstract units including handshape, location, movement, and palm orientation) can vary when it is realized phonetically (how these units are physically produced) – will have a positive impact on comprehension performance. Previous work has explored fingerspelling comprehension but this is the first to apply it to teaching practices. [SEP] Justify:

Output: The text supports the claim by indicating that explicit phonetic instruction, particularly regarding the structure of fingerspelling and how ASL phonology can vary phonetically, aims to improve comprehension performance. It states that the study assesses whether understanding the variations in phonetics will positively impact students' comprehension of fingerspelling, which aligns directly with the claim that phonetic variations influence 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: Such innovations can also aid in rebounding from challenging periods and offer numerous other benefits. Building on vast experiences and initiatives in this sphere, we can outline several insights and provide guiding principles: Regarding the numerous data collection technologies, those that identify thelocation of people, or similarly, any type of phenomenon, are highly relevant for the geography of tourism. New technologies and tracking techniques, including GPS, are emerging. Intelligence and expansive data analytics can addressthe prevalent issue of fragmentation in tourism-oriented mobile applications. A common challenge arises when such apps overly specialize, lacking in holistic personalization. This limitation often stems from their reliance on isolated datasets, rather than catering to the comprehensive requirements of the stakeholders. Considering the pivotal role communication plays within the tourism sec-tor, beyond mere content analysis, there’s potential to deploy generative models rooted in to bolster content generation endeavours. Thus, the advent of has significantly changed the perspective towards the applications of computational power in everyday life. Thanks to the strong collaboration of Statistics, Computer Science, and Engineering, machines can now adapt to the evolving environment and easily optimize problems that were previously non-computational. Frequent developments in the field of data mining and have further boosted the applications of computing power in more diverse fields. Therefore, this procedure is defined as a ”data mining” technique that transforms original data into a format readable and understandable by the model. Data preprocessing, is the first step that marks the beginning of the construction process of a model. Typically, real-world data is imprecise, incomplete, inconsistent (contains outliers or errors), and often has null values referring to specific attributes. Data preprocessing is pivotal as it refines, structures, and arranges unprocessed data, priming them for integration into (ML) algorithms. Serving as the foundational framework for Artificial Intelligence, ML frequently forms the bedrock for data science initiatives, intertwining aspects of computer science and statistical methodologies. The objective is to cultivate algorithms and theoretical constructs rooted in statistical paradigms, targeting elevated predictive prowess (Jordan - Mitchell, 2015). Consequently, ML algorithms construct predictive frameworks adept at correlating user attributes to specific classifications (or determinations) following a learning process. [SEP] Justify:

Output: The text contrasts the claim by highlighting the challenges and limitations associated with data mining and machine learning. It emphasizes that real-world data is often 'imprecise, incomplete, inconsistent (contains outliers or errors), and often has null values', suggesting that perfect accuracy in predictions is unattainable due to these issues. Additionally, it discusses the importance of data preprocessing to refine and structure the data, implying that the process is complex and does not guarantee perfect predictive accuracy.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: Science 304:1623–1627. (2005) Forest soils and carbon sequestration. and Management 220:242–258. (2014) Agroforestry systems: helping smallholders adapt to climate risks while mitigating climate change. 5:825–833. Lawrence D, (2015) Effects of tropical deforestation on climate and agriculture. Change 5:27–36. (2018) Tree size and climatic water deficit control root to shoot ratio in individual trees globally. 217:8–11. Lorenz K, (2014) Soil organic carbon sequestration in agroforestry systems. A review. Agronomy for 34:443–454. Lundgren B, (1982) Sustained agroforestry. In: Agricultural research for development: Potentials and challenges in asia. pp 24–29 (2020) Carbon accumulation in agroforestry systems is affected by tree species diversity, age and regional climate: A global meta-analysis. and Biogeography 29:1817–1828. Madejón P, Alaejos J, García-Álbala J, et al (2016) Three-year study of fast-growing trees in degraded soils amended with composts: Effects on soil fertility and productivity. Journal of 169:18–26. (2021) Tree diversity and carbon stocks in agroforestry systems in northern Ethiopia. and Management 16:14. (2023) World reference base for soil resources (WRB). In: Encyclopedia of soils in the environment. Elsevier, pp 206–217 Marín‐Spiotta E, (2013) Carbon storage in successional and plantation forest soils: a tropical analysis. and Biogeography 22:105–117. (2020) Land‐use history determines ecosystem services and conservation value in tropical agroforestry. 13: (2013) Carbon pools recover more quickly than plant biodiversity in tropical secondary forests. Proceedings of the B: 280:20132236–20132236. Matus FJ (2021) Fine silt and clay content is the main factor defining maximal C and N accumulations in soils: a meta-analysis. 11:6438. (2007) Database - (2017) Soil carbon 4 per mille. Geoderma 292:59–86. [SEP] Justify:

Output: The text does not directly address the claim regarding human memory and its capacity. Instead, it discusses various studies related to agroforestry, soil carbon storage, and environmental management, which are unrelated to the specifics of human memory.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: Geer and Keane (2014) and Keane and Geer (2016) suggest that perhaps one reason adults acquiring ASL as a second language struggle with understanding fingerspelling is related to incorrect cue-weighting. This problem has been noted for learners of spoken languages as well, and explicit instruction has been found to be successful in helping students to improve. That technique is therefore assessed here with ASL learners. This study examined two research questions, which will be summarized and discussed in Section IV. 1. Section IV. 2 addresses ways in which the present study can be extended in the future through additional studies. 1 Summary of resultsWhile explicit training has been shown to be effective for users of spoken languages acquiring a second spoken language, the same has yet to be shown for learners of a second language in a new modality. The first research question asks whether the explicit training will result in a more significant improvement in fingerspelling comprehension scores than students who were trained implicitly. This was in fact the case. Students in the explicit training group improved significantly from the pre- to post-test. The second question asked whether the explicit training would impact student performance on all experimental conditions equally. Because the training focuses students’ attention on information contained in the transition segments, information that previous work has shown students are not skilled at making use of, it was hypothesized that the training would result in a more significant improvement in the transitions-only condition. This was not found to be the case, but that could be due to a lack of sufficient power to test that specific question. In a larger follow-up study with 80 participants, Geer (2016) found a significant improvement in the transitions-only condition – for items containing letters with non-default palm orientation – from pre- to the first post-test and again from the first post-test to the second, suggesting that the reason for the null result in the present study is in fact due to the small number of participants. 2 Future workWhile the current work offers encouraging results, it raises additional questions, which will have to be addressed in future experiments. These can be divided into two main categories: (1) work that modifies the training program, and (2) work that assesses the comprehension abilities of signers from various backgrounds including already skilled signers who are Deaf and hearing and have acquired ASL at various ages, and also students at different levels of the learning process. Both of these is discussed briefly. [SEP] Justify:

Output: The text corroborates the claim by discussing how 'incorrect cue-weighting' in fingerspelling comprehension relates to phonetic variations and the challenges faced by learners of ASL as a second language. This indicates that phonetic variations do indeed influence comprehension in the context of sign language, similar to how they affect spoken languages, thereby supporting the claim.