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: Decision-making models often exploit very sophisticated algorithms that allow for excellent performance in terms of accuracy of results; unfortunately, excellent performances are often obtained with very complex models, such as Networks, which are difficult to interpret even for experts in the field. Artificial Intelligence, big data, and are often cited concerning the currently popular terms of ”smart tourism” and ”smart destinations”. Over the past several years, tourism has emerged as a crucial component of a nation’s economic framework. The appetite for tourism has demonstrated consistent augmentation. Yet, this domain has witnessed periodic oscillations, attributed to the unpredictability of influential variables and external intrusions. Academics, industry experts, and decision-makers have exhibited heightened interest in the patterns of tourism growth and demand trajectories, endeavoring to anticipate upcoming tourist influxes. Predominantly, research endeavours targeting tourism demand projections have centered on international tourist movements. This is largely because international tourism data is more systematically recorded compared to its domestic counterpart, with only a handful of studies delving into domestic tourism dynamics. 2 and tourism in literatureIn the past several years, tourism has solidified its position as a central pillar within a nation’s economic structure. The appetite for travel and exploration has consistently risen. Yet, this sector has faced intermittent disturbances due to unpredictable influencers and unforeseen events. Scholars, industry specialists, and regulatory figures have extensively scrutinized the evolutionary patterns and consumption tendencies in tourism, with aspirations to foresee subsequent tourist migrations. The majority of research endeavours zeroing in on tourism demand anticipations have predominantly concentrated on international travel movements. This inclination is primarily because data on international tourism tends to be more detailed compared to domestic travel insights, with a limited number of investigations touching upon domestic tourism nuances. Precise prognostications become indispensable for locales where authorities seek to either harness the momentum in the tourism sector or maintain a harmonious equilibrium between environmental and societal demands. In such scenarios, those forecasting international tourist demand have endeavoured to encompass overarching conditions of source markets, target locales, and even adjacent or rival territories that could sway their tourism influxes. The body of work concerning tourism demand prediction is substantial, as an initial overview suggests. A significant portion of these studies has been dedicated to anticipating international tourist movements using a myriad of quantitative techniques (Song et al. , 2019), encompassing time series, econometric approaches, and methodologies. [SEP] Justify:

Output: The text contrasts the claim by highlighting the unpredictability inherent in tourism dynamics and the challenges in achieving perfect accuracy in predictions. It discusses the complexities of using sophisticated AI models, emphasizing that despite the effectiveness of these models, they often result in high accuracy that is not perfect due to 'the unpredictability of influential variables and external intrusions.' This indicates that while AI can assist in forecasting, it cannot guarantee 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: Molyneux N, Cruz GR da, Williams RL, et al (2012) in Timor Leste: Implications for Food Security. AMBIO 41:823–840. (1993) An introduction to agroforestry. Wageningen. (2021) An introduction to agroforestry: Four decades of scientific developments. (1996) Ligniculture et maintien de la fertilité des sols dans l’Ouest-Cameroun. Bois et Forêts des Tropiques 249:33–49 Government, Australia (2013) (2024) Recovery of aboveground biomass, soil carbon stocks and species diversity in tropical montane secondary forests of East Africa. and Management 552:121569. Page-(1999) Comparison of methods for determining bulk densities of rocky forest soils. Society of 63:379–383 (2001) Organic inputs for soil fertility management in tropical agroecosystems: application of an organic resource database. Agriculture, Ecosystems & Environment 83:27–42. (2022) Agroforestry: Opportunities and Challenges in Timor-Leste. Forests 13:41. (1996) Les parcs à faidherbia. Cahiers scientifiques du Cirad-Forêt (2022) Disentangling the numbers behind agriculture-driven tropical deforestation. Science 377: (2019) Agricultural and forestry trade drives large share of tropical deforestation emissions. Change 56:1–10. Perfecto I, (2008) in Tropical Agroecosystems. Annals of the Academy of Sciences 1134:173–200. (2017) Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and rock fragment content. SOIL 3:61–66. (2021) Multidimensional tropical forest recovery. Science 374:1370–1376. R (2023) R: A language and environment for statistical computing. R Foundation for (2006) Temperature influences carbon accumulation in moist tropical forests. Ecology 87:76–87. (2015) Assessment of soil organic carbon stocks and fractions under different agroforestry systems in subtropical hill agroecosystems of north-east India. 89:677–690. [SEP] Justify:

Output: The text provided does not address the claim regarding human memory and its capacity to store information. It primarily consists of citations from academic articles related to agriculture, soil carbon stocks, and forestry, without any mention or relevance to human memory or its limitations.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: a Training modificationVarious researchers (including Holt & Lotto, 2006; Giannakopoulou et al. , 2013; Ylinen et al. , 2010) have noted that a crucial aspect of teaching foreign language segment identification and discrimination is exposure to variation. Through exposure to a wide range of talkers, students are better able to learn about the range of possible phonetic realizations of phonological segments, and how those segments come together to form words. This is also likely the case for ASL. Students in an experiment assessing the efficacy of fingerspelling curriculum lamented the fact that there was only one signer in the training (Thoryk, 2010). This suggests that one way to vary the explicit training in the future is to include fingerspelling examples from more than two signers, which it has currently. As has been shown for learners of a second spoken language, it is likely the addition of more variation in signers presenting fingerspelled tokens would further help students improve their comprehension. Another way to modify the training program is to expose students more to transition-only stimuli. Geer and Keane (2014), Keane and Geer (2016), Geer (2016) and the work detailed here all show that students perform better in the holds-only condition as compared to the transitions-only condition. Were they to be trained specifically on these stimuli, it may force them to be attentive to cues they otherwise ignore or have difficulty using, and will help them learn to attend to cues they should weight more heavily. This has been shown to be the case in L2 learners (Giannakopoulou et al. , 2013). b Perceiver skill levelA synthesis of work on Deaf signers, adults, and children suggests that understanding fingerspelling is not simply understanding ‘the sum of its letters’ (Akamatsu, 1985). Skilled signers can provide ASL translation equivalents for words fingerspelled to them but cannot always spell them back (Hanson, 1981), suggesting comprehension without reading letter-by-letter, and they can use information contained in the transition segments to identify the shape of masked letters (Schwarz, 2000). Geer and Keane (2014), Keane and Geer (2016), and the present study show that students seem to use a different approach, which relies heavily on attention to only the hold portions of the signal. [SEP] Justify:

Output: The text corroborates the claim by indicating that exposure to variation in phonetic realizations aids learners in understanding and discriminating language segments. It specifically states that 'through exposure to a wide range of talkers, students are better able to learn about the range of possible phonetic realizations of phonological segments.' This suggests that variations in phonetics are crucial for improving comprehension in both spoken and signed languages.


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: Conventional time series frameworks, such as Na¨ıve 1 models, Na¨ıve 2 models, exponential smoothing techniques, and basic AR models (Song - Li, 2008; Wu, Song - Shen, 2017), frequently serve as reference points in tourism projection analyses. Depending on the temporal data’s periodicity, ARIMA and configurations are predominantly employed. The literature has also explored several adaptations of the ARIMA structure. In contrast to non-causal time series configurations, econometric frameworks facilitate the exploration of correlations between tourism demand and its primary influencers, and such insights can be pivotal for policy guidance. Several crucial elements have been pinpointed in the literature as having a bearing on tourism demand, including traveler income, cost disparities between tourist destinations and origin nations, pricing in rival destinations, and prevailing currency exchange metrics. Beyond the realm of time series and econometric techniques, a plethora of methodologies grounded in have made their mark in tourism forecasting literature. Foremost among these is the Network (ANN) paradigm. This model is structured around multiple tiers, with each potentially comprising several neurons. The ANN approach, inherently non-parametric and driven by data, is adept at modeling intricate nonlinear associations. It stands out as the most recurrently deployed AI-centric method in tourism demand prediction endeavors. strategies represent evolved versions of artificial neural networks, characterized by a dense web of interlinked processing strata. For example, Law, Li, Fong - Han (2019) harnessed Deep Learning, integrating an attention mechanism to architect a grand-scale neural network. Nonetheless, there exists a challenge with the prevailing methodologies remains that these advanced models often face the problem of overfitting. As alluded to earlier, with the swift progression of the tourism sector, the anticipation of tourism demand has ascended in importance for both governmental bodies and commercial entities. As highlighted by the Organization (UNWTO), the ascent of international tourism consistently overshadows global economic growth, positioning the tourism industry as a pivotal catalyst for worldwide economic enhancement and progression. Consequently, the prediction of tourism demand has been under intensified scrutiny over recent years, particularly considering the fleeting essence of tourism offerings. Precise forecasting empowers industry participants to strategize proactively, optimizing resource distribution. Furthermore, enterprises can recalibrate their operational approaches to amplify their efficacy. [SEP] Justify:

Output: The text contrasts the claim by indicating that while AI methods, such as artificial neural networks (ANN), are utilized in tourism demand prediction, the text also highlights limitations, specifically that 'these advanced models often face the problem of overfitting.' This suggests that predictions made by AI are not perfectly accurate, as overfitting can lead to predictions that do not generalize well to new data.


Input: Task: pass the claim using the given text. Claim: Human memory can store infinite amounts of information. [SEP] Text: Réjou‐Méchain M, Tanguy A, Piponiot C, et al (2017) : an R package for estimating above‐ground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution 8:1163–1167. (2009) Changing drivers of deforestation and new opportunities for conservation. 23:1396–1405. Sayer EJ, Lopez-Sangil L, Crawford JA, et al (2019) Tropical forest soil carbon stocks do not increase despite 15 years of doubled litter inputs. 9:1–9. (2001) Carbon cycling and soil carbon storage in mesic to wet Hawaiian montane forests. Ecology 82:3182–3196. (2018) Agroforestry systems: Meta‐analysis of soil carbon stocks, sequestration processes, and future potentials. & Development 29:3886–3897. (2023) The positive effect of plant diversity on soil carbon depends on climate. 14:6624. (2023) Priority science can accelerate agroforestry as a natural climate solution. Change 13:1179–1190. (2012) Carbon content of tree tissues: A synthesis. Forests 3:332–352. (1992) Are tropical agroforestry home gardens sustainable? Agriculture, Ecosystems & Environment 41:189–207. (2018) Low‐cost agricultural waste accelerates tropical forest regeneration. 26:275–283. (2020) communication under the on (2022) World population prospects 2022. Department of Economic and Social Affairs: (2007) Climate change: Linking adaptation and mitigation through agroforestry. Mitigation and for 12:901–918. van (2014) Agroforest’s growing role in reducing carbon losses from Jambi (Sumatra), Indonesia. Change 14:825–834. (2012) Water availability is the main climate driver of neotropical tree growth. PloS one 7:e34074. (2019) Soil organic carbon storage as a key function of soils - A review of drivers and indicators at various scales. Geoderma 333:149–162. World-Bank SD department EA, (2009) Timor-Leste: - (2009) Global wood density database (2005) Texture and organic carbon relations described by a profile pedotransfer function for soils. Geoderma 127:168–173. [SEP] Justify:

Output: The text does not address the claim regarding human memory and its capacity to store information. It consists solely of citations and references related to ecological studies and does not provide any information about human memory or its limits.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: It would be useful, however, to better understand the ways in which skilled versus L2 learners diverge in fingerspelling comprehension, to test a wider range of signers with the paradigm employed here. Native signers, as well as those who acquired ASL at various points in their life but are now all highly skilled, should be tested in this paradigm to confirm that the divergence is in fact due to differences in ability to use cues provided in the hold versus transition segments. Impressionistically, when these stimuli have been shown to skilled signers, both Deaf and hearing, native and late learners, they have not been able to appreciate a difference in the two conditions and appear to understand them equally. This suggests that their performance is unlikely to differ significantly across conditions, as was the case for the ASL students. It may also be prudent to test ASL students at various stages of their learning process and also to vary the time at which the explicit training is introduced. It could be the case that reliance on hold segments is a characteristic of learners in their first and second years of ASL acquisition, but not of students in their third and fourth years, for example. The optimal time to introduce the explicit training can also be assessed in future work. V Concluding remarksThe overarching goal of this project has been to use information about how students understand fingerspelling, a challenging aspect of ASL acquisition, to assess the efficacy of a pedagogical tool designed to address this challenge. At the conclusion of the experiment, the first author met with study participants to discuss their experience. They were told a follow-up study would be conducted in the near future and that their feedback could be useful for preparing it. All students indicated they felt their training was beneficial to them. Students in the explicit training group said they appreciated being made aware of the ‘inner workings’ of fingerspelling. They felt it made them better prepared to appreciate fingerspelling as a whole, rather than letter-by-letter, which is what skilled signers are doing already. This is an interesting remark because many ASL teachers instruct students to ‘sound words out’ rather than stating the name of each letter in their heads, because it is possible to correctly identify a long string of letters, but not understand the whole word. The goal of this instruction is to encourage students to see the whole word and not each of the letters, but it seems phrasing it in this way is ineffective. [SEP] Justify:

Output: The text corroborates the claim by discussing how variations in fingerspelling comprehension among different learners, particularly skilled signers and ASL students, can impact understanding. It notes that reliance on different cues during fingerspelling, such as hold segments, may vary based on learners' experience and training, which directly relates to the influence of phonetic variations on language comprehension. Additionally, the text suggests that effective teaching approaches that enhance comprehension are important, reinforcing the idea that phonetic variations are significant in understanding 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: In the pursuit of refining forecast precision, innovative methodologies are ceaselessly being cultivated in the domain of tourism demand prediction. 3 and ANN (Network), a theoretical overviw 3. 1 is a subcategory of and refers to the branch of artificial intelligence that uses algorithms inspired by the structure and function of the brain called artificial neural networks. Scientifically speaking, we could say that is the learning of systems through data learned using algorithms. Indeed, is a subset of the expansive spectrum, centered around the absorption and interpretation of data representations, rather than deploying algorithms tailored for distinct tasks. Applications of architectures extend to realms such as Computer Vision, automated speech recognition, natural language interpretation, audio detection, and bioinformatics (which entails the utilization of computational tools to quantitatively and statistically delineate specific biological events). can be characterized as a system that harnesses a cadre of machine learning techniques that: Employ several tiers of interconnected non-linear units dedicated to executing tasks related to feature distillation and modification. Every subsequent layer draws upon the output from its predecessor as its input source. These algorithms span both supervised and unsupervised categories, with their applications encompassing pattern evaluation (under unsupervised learning) and categorization (within supervised learning). Operate on the foundation of unsupervised learning across multiple stratified layers of data attributes (and their portrayals). Features at an elevated level are formulated from those at more foundational levels, culminating in a tiered representation. Belongs to the expansive category of representation learning algorithms nestled within the domain of Machine Learning. Acquire various tiers of representation aligning with distinct abstraction degrees; these layers coalesce into a conceptual hierarchy. Through the implementation of Deep Learning, we secure a system that independently orchestrates data classification and hierarchical arrangement, pinpointing the most pertinent and beneficial data for problem resolution (mirroring human cognitive processes), and enhancing its efficacy through perpetual learning. By integrating Deep Learning, we essentially equip ourselves with a ”system” that possesses the capability to independently categorize and hierarchically organize data, discerning the most pertinent and advantageous information for issue resolution, and progressively enhancing its efficiency through ongoing learning. Much like the human brain employs its neurons to craft a reaction, tackle challenges, or infer a logical proposition, ultimately establishing our neural configurations. analogously employs artificial neural networks. [SEP] Justify:

Output: The text contrasts the claim by highlighting that AI and specifically artificial neural networks (ANN) utilize sophisticated algorithms and learning systems that are designed to improve data classification and organization, but it does not assert that these systems can predict the future with perfect accuracy. The detailed explanation of deep learning and its processes indicates that while AI systems can enhance their ability to interpret data, they are still fundamentally reliant on existing data and patterns rather than guaranteeing precise future predictions.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 21022 Open access Copyright information Version of RecordFebruary 6, 2017 Accepted ManuscriptDecember 28, 2016 Open annotations (there are currently 0 annotations on this page). 13,748 views 1,962 downloads 224 1022. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Additional files 13 figures, 7 videos and 1 additional file 1 with 1 supplement Download asset Open asset Introduction to lobula columnar (LC) neurons. Schematics (A–C) and confocal images (D–K) show the lobula and adjacent parts of the visual system. (A,D,G,H) Horizontal sections. (B,E,I,J) Anterior views. (C,F,K) Cross-section views of the lobula. Some subregions of the optic lobe (Me, Medulla; Lp, Lobula plate; Lo, Lobula) and central brain (; ) are indicated in selected panels. Dendrites of individual LC neurons (red and blue cells in the schematics and red, blue and green cells in J,K) span only part of the visual field. As populations, the neurons of a given LC cell type cover most or all of the lobula (F), though LC neurons with regionally restricted lobula arbors also exist (e. g. LC14 (Otsuna and Ito, 2006), see Figure 1—figure supplement 1). LC neurons receive feed forward visual inputs from photoreceptors in the retina via a series of optic lobe interneurons (a few lamina neurons, in brown, and transmedullary neurons [Tm], in green, are illustrated as examples in [A]). This places LC neurons at least 2–3 synapses downstream of the photoreceptors. The majority of LC neurons projects to distinct target regions in the central brain called optic glomeruli; some of these are illustrated in (A) and (B) and also visible as distinct structures in the anti-Brp pattern in the images in (D) and (E). Most optic glomeruli are located in the PVLP and the adjacent more posterior PLP. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the anatomical and functional aspects of lobula columnar neurons in the visual system, without addressing cognitive processes or cultural differences. There is no indication of any connection to the claim regarding cognitive processes across cultures.


Input: Task: corroborate the claim using the given text. Claim: Phonetic variations can influence language comprehension. [SEP] Text: So, while teachers tell students to ‘sound out’ fingerspelled words, they are not giving them the tools to learn how to do this. Worse still, as Li and Juffs (2015) suggested, how students are taught (static posture by static posture) could be hindering their ability to see the forest (whole words) instead of the trees (individual letters). Students who received the explicit training said it helped to shed light on that. In addition, students expressed frustration with phonetic variation they encountered in fingerspelling prior to the study. ‘It’s hard when one signer produces [some fingerspelled letter] like this [showing some variant of the letter], while other signers do it like that [showing some other variant of the letter]. ’ Arming them with knowledge of the types of variation they might encounter helped them feel more secure in tackling fingerspelling comprehension tasks. The results of this experiment indicate that ASL teachers should begin to incorporate some type of phonetic instruction in their curricula. Students not only perform better but also feel more confident and empowered in their fingerspelling comprehension. Phonetic instruction for lexical signs may also be useful, although this has yet to be tested. The results of this experiment are encouraging that this may be the case and should be the topic of future study. 1. We draw the comparisons to spoken language studies not because they are the closest parallels, but because there simply are no other studies (or only very few) that do this in the sign modality. We fundamentally believe that fingerspelling and spoken languages have phonetics (and phonetic variation), and that this phonetic variation can be one source of struggle for non-native speakers, and the purpose of this investigation is to test help with that. 2. Testing skilled signers with this paradigm is underway. The implications of the expected results for the present work are addressed in the discussion. 4. Table 4. Table of predictor variables and their reference levels. PredictorNumber of levelsReference levelTest-type2Pre-testGroup2(implicit training)Condition4Clear AFor the test-type predictor, the pre-test is the reference level because the experiment is assessing how much change there is from pre-test to post-test. This means that if participants improve from pre-test to post-test, the number should be positive, or a positive number of greater magnitude than the pre-test. [SEP] Justify:

Output: The text corroborates the claim by discussing how phonetic variations in fingerspelling can affect students' comprehension of the language. It describes frustration among students due to differing phonetic representations from various signers, indicating that these variations can hinder their understanding. Furthermore, the text suggests that incorporating phonetic instruction can aid in addressing these difficulties, which aligns with the idea that phonetic variations 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: Such systems are ”malleable”, able to adapt their architecture (inclusive of nodes and interlinkages) contingent upon both external input and the intrinsic data traversing the neural network during the assimilation phase. Artificial neural networks are structured with multiple latent layers, often termed as hidden layers, encompassing a flexible count of nodes. Each node, or synthetic neuron, interlinks with others, bearing a specific weight and a designated threshold. Should the output from any distinct node surpass the set threshold magnitude, that node is triggered, forwarding data to the subsequent network tier. If not, the data remains stagnant, not progressing to the ensuing layer. 3. 2 ANN - neural networks, as depicted in Fig. 3 (Deep Learning: Foundations and Concepts; Christopher M. Bishop, − 2023), are mathematical constructs comprising synthetic neurons, designed in the vein of biological neural systems found in humans or animals. These networks are harnessed to address engineering challenges spanning multiple tech-centric domains like informatics, electronics, simulations, among other specializations. To delve deeper, neural networks can be elucidated as mathematical-digital frameworks mirroring the operations of organic neural systems. Essentially, these models revolve around intricate interlinkages of information. Such interlinkages spring from synthetic neurons and computation regimes grounded in the cognitive paradigm termed ”connectionism. ” This refers to the PDP – Processing approach to information handling: akin to how the human cortex processes sensory inputs concurrently and diffuses this data throughout the expansive network nodes. This is the crucial point underlying neural networks in the learning phase: a parameter, whether it’s the weight or the offset of the network, is varied to approach ever more accurate and precise prediction models. The set of weights and the offset value constitute the bias and represent precisely the information that the neuron learns during training and retains for subsequent reuses. The perceptron, although approaching this ideal concept, is not the best of artificial neurons, given that it is a binary classifier that therefore outputs 0 and 1: this detail is limiting because it will not allow us to observe a significant change in the model but only a variation due to the modification of the biases. The artificial neuron that best assimilates this behavior is instead the Sigmoid Neuron, represented in the same way as the perceptron, and which can therefore have up to n inputs (x1, x2. . [SEP] Justify:

Output: The text contrasts the claim by explaining the complexities and limitations of artificial neural networks. It describes how these systems are 'malleable' and adapt their architecture based on various inputs, indicating that predictions made by AI are not inherently perfect. The discussion on how the performance of artificial neurons, particularly the perceptron and the Sigmoid Neuron, reflects that there are constraints and variabilities in AI's ability to predict outcomes. The mention that the perceptron is a binary classifier and that significant changes in the model require nuanced adjustments to weights and biases suggests that AI cannot achieve perfect accuracy in predictions.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: The more dorsal AOTu (illustrated in [B]) is considered a specialized optic glomerulus. For a more detailed map of optic glomeruli see Figure 3. Confocal images show either populations of neurons (D–I) or individual cells labeled using Multicolor FlpOut (MCFO) (Nern et al. , 2015) (J,K). LC cell types shown are LC17 (D,G,H) and LC16 (E,F,I–K). Population labeling (D–I) was with split-GAL4 driven expression of a membrane marker (green; myr::smFLAG, using pJFRC225-5XUAS-IVS-myr::smFLAG in VK00005) with a presynaptic marker also shown [magenta; synaptotagmin-HA, using pJFRC51-3XUAS-IVS-syt::smHA in su(Hw)attP1] in (D,E) and by itself in (H). A neuropil marker (anti-Brp) is included in grey in (D–F,K) and neuropil regions are also in grey in the schematics. Images in (D,E,G–J) were generated using brains that were computationally aligned to a template brain using the anti-Brp pattern as reference. The anti-Brp pattern in (D,E) is that of the standard brain used for alignment. Images in (D–K) show projection images of different views of three-dimensional image stacks; these were generated in either Fiji ( (D,E,G–J) or Vaa3D (Peng et al. , 2010) (F,K). Scale bars represent 20 µm. Figure 1—figure supplement 1 Download asset Open asset Examples of additional LC cell types and similar neurons. These cell types were not selected for further analyses in this study since they did not project to glomerular target regions in the ventrolateral central brain, covered only part of the lobula as cell populations or are VPNs of a different optic lobe region. (A,B) LC14 cells, which project to the contralateral lobula, have been previously described (Hassan et al. , 2000; Otsuna and Ito, 2006). [SEP] Justify:

Output: The text provides a detailed description of neuroanatomy and experimental methods related to neurons and brain imaging, which does not address the claim regarding the universality of cognitive processes across different cultures. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Neurolinguistic programming explores the connection between language and thought. [SEP] Text: 5 Tomography (PET) 3. 2. 6 Near-(NIRS) 3. 2. 7 Facial coding 3. 2. 8 (EMG) 3. 2. 9 Eye-tracking 3. 2. 10 Measurement of physiological responses 3. 3 Neuromarketing in the context of the research 4 Ethics of neuromarketing research 5 (NLP) 5. 1 Neurological levels 5. 1. 1 Surrounding 5. 1. 2 Behavior 5. 1. 3 Abilities and skills 5. 1. 4 Values and beliefs 5. 1. 5 Identity (mission) 5. 1. 6 Connection (mission, transmission, meaning) 5. 2 Communication patterns based on NLP 5. 2. 1 Mind reading 5. 2. 2 Agreeable attitude 5. 2. 3 A sequence of several affirmative statements or questions 5. 2. 4 Hyptnotic accompanying and guidance 5. 2. 5 Breaking the formula with NLP 5. 2. 6 Reporting negative things 5. 2. 7 The pattern of awareness in NLP 5. 3 NLP techniques 5. 3. 1 Mind mapping 5. 3. 2 Brain gym 5. 3. 3 Disney’s creative strategy 5. 3. 4 The six thinking hats model 5. 3. 5 NLP metaprograms 5. 3. 6 Reframing 6 NLP and business 6. 1 Cognitive-emotional-behavioral factors in NLP 6. 1. 1 Emotions as a mean of communication with the customer 6. 1. 2 Emotions as a decision-making support 6. 2 NLP as a tool of successful sales 6. 3 Main principles of NLP in managerial practice and business 6. 3. 1 Communication is continuous 6. 3. 2 The meaning of communication is the reaction it generates 6. 3. 3 People react to their idea and model of reality, not to reality itself 6. 3. 4 Body, spirit, and soul are different, closely connected partial aspects of the whole person. Changing one part affects the others 6. 3. 5 The most flexible person wins 6. 3. 6 Unhappy, sick people are not broken – they use what they know and have, but in a way that doesn’t satisfactorily meet their needs 6. 3. 7 Everyone acts as best they can at the given moment 6. 3. 8 Every behavior is valuable and useful in some context 6. 3. [SEP] Justify:

Output: The text corroborates the claim by discussing various aspects of Neurolinguistic Programming (NLP), including communication patterns and techniques that relate to the connection between language use and cognitive processes. For example, it mentions 'Communication patterns based on NLP,' suggesting a direct exploration of how language influences thought and behavior.


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: xn) with their respective weights (w1, w2. . wn) and depends on the offset b; it has an output y, which can be applied again as input for other neurons on the network: the substantial difference from the perceptron is that the values that the inputs and outputs can assume are values that oscillate between 0 and 1. This will allow modeling of realistic systems. A neural network is therefore composed of an input layer and an output layer (Fig. 4 - Deep Learning: Foundations and Concepts; Christopher M. Bishop, − 2023), which in the figure are the outermost layers, while the internal layers depend on the type of network implemented and determine the depth of the network. The number of inputs depends on the purpose and implementation choices, while the output layer varies based on the number of output values of the model, and the presence of multiple output neurons can make the result more or less accurate. An output, in this structure, can be sent as input to the next layer, creating feed-forward networks as long as there are no cycles, so the information is always sent forward. If hypothetically we created cycles, we could speak of Networks (RNN), which are closer to human behavior but are less used in terms of application, computation, and results and will not be covered in this research. 4 Designing and training a neural network to simulate a prediction in the tourism field: an example based on hotel reviews. Tourism, with its dynamic nature and constant evolution, stands today at the crossroads between tradition and innovation, where new digital technologies are reshaping how travel experiences are shared and perceived. In this vein, the current 4. 1 Dataset introduction In the quest to harness the vast expanse of user-generated content within the hospitality industry, our study employs a comprehensive dataset derived from over 515,000 hotel reviews across Europe, meticulously compiled and made accessible for analysis. This rich corpus of text, sourced from a diverse array of hotels provide a fertile ground for applying advanced computational techniques to unravel patterns in customer sentiment and expectations. The foundation of our analytical framework is built upon a stack of sophisticated Python libraries, each chosen for its proven efficacy in data science and machine learning tasks. NumPy, a cornerstone for numerical computing, facilitates operations on large multi-dimensional arrays and matrices—a quintessential aspect of linear algebra required in our data processing endeavors. [SEP] Justify:

Output: The text contrasts the claim by discussing the nature and complexity of neural networks and their applications in prediction tasks. It explains that while neural networks can model realistic systems and contribute to predictive analytics, it does not assert that these predictions are made with perfect accuracy. Instead, it highlights factors such as the number of inputs and outputs affecting the accuracy of results and acknowledges the dynamic nature of fields like tourism, which further complicates predictive modeling.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: LC14 is the name used by Otsuna and Ito; some closely related cells, here named LC14b, project to the contralateral medulla in addition to the lobula (Hassan et al. , 2000; Langen et al. , 2013). LC19 (C) and LC23 (D) are newly identified cell types that project to the contralateral (LC19) or both the ipsi- and contralateral (LC23) central brain. LC19 and LC23 neurons have cell bodies in the cell body rind between optic lobe and central brain near the dorsal tip of the lobula neuropile. LC19 arbors in the lobula are located mainly in layers Lo5 and Lo6, those of LC23 neurons primarily in layers Lo2 and Lo4 (see Figure 5—figure supplement 1 for details of lobula stratification). LC19 processes are present across the entire lobula; those of LC23 appear to cover only the anterior, not the posterior lobula. Images show projection views (generated in Vaa3D) of MCFO labeled neurons together with the anti-Brp neuropil marker (grey). (E) An example of a columnar lobula plate VPN (LPC1) also identified by others (Panser et al. , 2016). LPC1 cells have cell bodies in the lobula plate cortex. Their processes in the lobula plate are mainly in lobula plate layer Lp2. (A), (B) and (C) show multiple cells; (D) and (E) segmented single cells. Scale bar represents 50 µm. Figure 2 with 1 supplement Download asset Open asset Expression patterns of LC neuron split-GAL4 lines. Split-GAL4 driven expression of 20xUAS-CsChrimson-mVenus (insertion in attP18; visualized using anti-GFP antibody labeling; green) and a neuropil marker (anti-Brp, magenta) are shown. Genotypes are identical to those used in the behavioral experiments in Figure 8. Some adjustments of brightness and contrast of individual samples were made. For each driver line (but not across different lines), adjustments and microscope settings were identical for the brain shown in this Figure and the corresponding ventral nerve cord (VNC) shown in Figure 2—figure supplement 1. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the anatomical and functional characteristics of specific types of neurons without addressing cognitive processes or cultural differences. There is no mention of cognition or cultural variations in the provided text.


Input: Task: corroborate the claim using the given text. Claim: Neurolinguistic programming explores the connection between language and thought. [SEP] Text: 9 The more options a person can use to shape his life, the smoother and more satisfying his life will be 6. 3. 10 Everyone is capable of achieving everything – in their own way 6. 3. 11 Everyone has everything they need to handle life’s problems – they just have to learn how to use it appropriately 6. 3. 12 There is no such a thing as failure, only results 6. 3. 13 Problems taste better in smaller doses 6. 3. 14 Behind every problematic behavior is a good intention 6. 4 Pillars of NLP in practice 7 Research in the field of neuromarketing, business behavior and NLP 7. 1 Sensory marketing research as an attribute of neuromarketing and business behavior 7. 1. 1 The aim and hypotheses of the first research project 7. 1. 2 Methodologies and methods of the first research project 7. 1. 3 Research sample of the first research project 7. 1. 4 Results of the first research project 7. 1. 5 Summary results of the first research project 7. 2 Neuromarketing research and neurolinguistic programming 7. 2. 1 The aim and hypotheses of the second research project 7. 2. 2 Methodologies and methods of the second research project NLP-C methodology (Neuro-– Communication) NLP-T methodology (Neuro-– Techniques) NM-SSP methodology (Neuromarketing – Shop, Seller, Product) 7. 2. 3 Research sample of the second research project 7. 2. 4 Results of the second research project 7. 2. 5 Summary results of the second research project of bibliographical 1. 1. Personality of businessman Each person is unique and characterized by certain attributes of himself. Personality type can reveal how a person will react in life situations. Personality type is a set of qualities that occur together and are interconnected (Alexy, 2011). Human factors determine people’s behavior and influence their interactions. Marketers not only determine strategy based on their own perspectives but also receive opinions from others. For a trader, it is important to be conscious of his abilities and skills and to be able to admit a mistake, ask for help, or reveal that he is capable to do something by himself (Yuan et al. , 2017). Within sales management, salespeople are the most meaningful part of the company’s function. [SEP] Justify:

Output: The text corroborates the claim as it discusses various aspects of neurolinguistic programming (NLP), which inherently includes the relationship between language and thought processes. Specifically, the mention of methodologies and research in NLP suggests an emphasis on how language is used to influence behavior and thought patterns, thus supporting the idea that NLP explores these connections.


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 we venture further into the exploration of this dataset, we aim to not only predict hotel ratings but also uncover the underlying factors that influence customer satisfaction. The dataset acts as a portal through which we can peer into the collective experiences of travelers, drawing from their shared narratives to build a predictive model that is as informative as it is perceptive. To illustrate the geographical distribution of hotels within our dataset, we employ a sophisticated visualization technique that plots each hotel’s location on an interactive map. This is achieved through the integration of Python’s plotly library, renowned for its capability to create complex, interactive plots that are both informative and engaging. The first segment of the code defines a Scattermapbox trace, which is an object in plotly that represents a series of points on a map. Each point corresponds to a hotel, with its latitude and longitude derived from the data DataFrame columns ’lat’ and ’lng’, respectively. The hotels are displayed as markers, styled in cyan color with a slightly transparent appearance (an opacity of 0. 7) for visual appeal and to prevent the map from appearing overly congested. In addition, each marker is associated with the hotel’s name, which is displayed as a hover text. This feature enhances user interactivity, as it allows the viewer to hover over any marker on the map to reveal the name of the hotel at that specific location. Finally, a Figure object is created, combining the data trace and layout. This object encapsulates all the information needed to render the map. The iplot function, which is part of the plotly interactive plotting interface, is called to display the figure. This command activates the visualization in a web browser, presenting an interactive and user-friendly map that allows stakeholders to visualize the spatial distribution of the hotel data effectively. By leveraging interactive geospatial plotting, we provide a compelling visual representation of data that can significantly enhance the comprehensibility of the patterns and trends within the tourism domain. In an effort to elucidate the demographic distribution of the reviewers in our hotel review dataset, we conducted an analysis focusing on the nationalities of the contributors. Understanding the diversity of the reviewers provides insight into the global reach of the hotels in question and can potentially highlight cultural influences on review content and ratings. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of AI's predictive capabilities. It emphasizes the aim to build a predictive model regarding hotel ratings but does not assert that AI can predict the future with perfect accuracy. Instead, it highlights the complexities of understanding customer satisfaction and emphasizes trends and visualization without guaranteeing complete accuracy in predictions.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: The images in this and other figures with CsChrimson expression patterns are representative of 2–5 brains and 2–3 VNCs imaged for each split-GAL4 line. Scale bar represents 50 µm. Original confocal stacks are available from www. janelia. org/split-GAL4. Figure 2—figure supplement 1 Download asset Open asset VNC expression patterns of the corresponding brains shown in Figure 2. Imaging parameters and brightness or contrast adjustments were identical for each brain/VNC pair. Scale bar represents 50 µm. Original confocal stacks are available from www. janelia. org/split-GAL4. Figure 3 with 2 supplements Download asset Open asset LC neuron terminals in the central brain are organized into distinct neuropil structures. (A) Illustration of the projection patterns of 12 LC cell types that project to major optic glomeruli in the PVLP (or in the PVLP/PLP boundary region). Image is a substack maximum intensity projection of a composite image stack generated from 12 computationally aligned image stacks (one for each cell type). Images were manually segmented to exclude background and some off-target cell types. Unedited pre-alignment stacks are available from www. janelia. org/split-GAL4. For details of genotypes see Supplementary file 1D. (B,C) Target regions of the LC neurons shown in (A) match the optic glomeruli pattern in the PVLP. Target regions of different LC cell types were labeled by split-GAL4 driven expression of a presynaptic marker (pJFRC51-3XUAS-IVS-syt::smHA in su(Hw)attP1, detected using anti-HA antibody labeling). Images for different cell types were edited and combined as described above. The anti-Brp pattern of the standard brain used for alignment is shown in grey. (C) Pattern of optic glomeruli revealed by anti-Brp labeling. Image is the same as the anti-Brp channel of the overlay shown in (B). Note the close correspondence of the presynaptic terminals of LC cell populations and optic glomeruli. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses imaging and expression patterns related to a specific scientific study, without addressing cognitive processes or their cultural variations.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Services on 10. 4067/S0718-95162015005000019 ACC-deaminase and/or nitrogen fixing rhizobacteria and growth of wheat (L. ) W. Hassan1, 2*, M. Hussain2, S. Bashir3 A. N. Shah4, R. Bano5, J. David6 1Humboldt-Universität zu Berlin, Berlin, 14195, Germany. *Corresponding author: [email protected] 2College of Agriculture, B. Z. U. Bahadur Campus, Layyah, 31200, Pakistan. 3Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan. 4University, Wuhan, 430070, P. R. China. 5. 6Freie Universität, Berlin, 14195, Germany. growth promoting rhizobacteria (PGPR) are beneficial bacteria, which can enhance the growth of the plants, when applied to crops. A pot experiment was conducted to examine the effect of six PGPR isolates on the growth of wheat. Inoculation with rhizobacterial isolates increased the all measured physical, chemical and enzymatic growth parameters compared to control (CK). However, the WAN1 isolate had the highest effect, and significantly (P < 0. 05) increased the root length (3. 51-fold), shoot length (3. 22-fold), seedling fresh (3. 41-fold) and dry (3. 91-fold) weight, chlorophyll a (3. 90-fold), chlorophyll b (3. 51-fold), carotenoid contents (7. 23-fold), plant macronutrient uptake i. e. N (7. 20-fold, 6. 71-fold), P (7. 41-fold, 5. 01-fold), K (5. 51-fold, 3. 91-fold), Ca (6. 40-fold, 5. 21-fold) and Mg (5. 82-fold, 7. 11-fold) in shoot and root, plant micronutrient uptake i. e. Zn (6. 40-fold, 9. 11-fold), Cu (7. 31-fold, 7. [SEP] Justify:

Output: The text corroborates the claim by detailing how growth-promoting rhizobacteria (PGPR) can enhance the growth of plants, specifically in a pot experiment with wheat. It states that inoculation with rhizobacterial isolates increased various growth parameters significantly compared to the control group. This aligns with the claim about the positive effects of rhizobacteria on plant growth, although it doesn't explicitly mention nitrogen fixation, it suggests the function of these beneficial bacteria in enhancing growth.


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 analysis process was carried out following the steps described below: First, we removed the placeholder strings ’No Positive’ and ’No Negative’ from the positive and negative review fields, respectively. This step is crucial to ensure that the analysed texts reflect the reviewers’ true intentions. Next, we categorised the reviews into ’Goodreview’ or ’Badreview’ based on a score assigned by the reviewer. We considered reviews with a score of less than 7 as ’Badreview’ and those with a score of 7 or more as ’Goodreview’. As the class distributions were unbalanced, we proceeded with random sampling of the data to balance the number of ’Goodreviews’ with the number of ’Badreviews’. This allowed us to have a more balanced dataset for training the machine learning models. We encoded the review labels with a LabelEncoder to facilitate processing by the machine learning algorithms. Finally, the dataset was divided into training and test sets to validate the effectiveness of the prediction model. Data preprocessing and class balancing are essential steps to ensure that machine learning models are not biased towards a dominant class. This paves the way for the application of advanced classification techniques, which will be analysed in subsequent 4. 4 TF-IDF, short for term frequency-inverse document frequency, is a prevalent method in natural language processing (NLP) for converting text into numerical vectors suitable for machine learning models. This technique offers significant improvements over basic bag-of-words models, which simply tally term frequencies in documents. TF-IDF enhances the representation by adjusting the term frequencies based on their commonality across the entire text corpus. This adjustment diminishes the impact of frequently occurring words that might not contribute much to differentiating documents. Moreover, the inverse document frequency (IDF) component of TF-IDF emphasizes words that are distinct from specific documents, aiding in more effectively distinguishing between them. In essence, TF-IDF stands as an effective and popular approach for transforming textual information into numerical form for NLP applications. An LSA plot is a visualization tool in natural language processing (NLP) that illustrates the connections between documents and their associated topics. LSA, or Analysis, is a method used for reducing the complexity of textual data in NLP. [SEP] Justify:

Output: The text contrasts the claim by detailing the data analysis and preprocessing steps involved in machine learning models, without claiming perfection in predictions or indicating that AI can predict the future with perfect accuracy. It outlines various methodologies and techniques, such as TF-IDF and LSA, that enhance the representation of textual data, but does not support the notion that AI's predictive capabilities are flawless.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: Asterisk marks a large synapse rich (based on anti-Brp labeling) glomerular structure in the PLP that appears to be the target of several columnar VPNs that were not included here since we considered them to be primarily associated with the lobula plate, not the lobula (though some have lobula branches). As an example, one cell of one such type (LPC1) is shown in Figure 1—figure supplement 1E. LPC1 was also identified by (Panser et al. , 2016). (D–F) Overlays generated as in (A) and (B) showing the projection patterns (D) and target regions (E,F) of additional LC neurons with terminals in the posterior PVLP and in the PLP. LC24, LC25 and LC26 projected to locations close to those of LC15, LC6 and LC16 but slightly more posterior and might also slightly overlap with each other. In particular, LC25 was unusual in that its terminals spread along the surface of, and perhaps partly overlapped with, the LC15 target region and other adjacent glomeruli (E and Figure 3—figure supplement 2). Similarly, part of the boundary of the LC22 target region, as visualized by synaptic marker expression in LC22 cells, was less well defined than the boundaries of most other glomeruli (F). The LC22 glomerulus also appears to overlap with the target region of a second columnar VPN: stochastic labeling experiments revealed an additional LPLC-like cell type (distinct from LPLC1 and LPLC2 and tentatively named LPLC4 in agreement with [Panser et al. , 2016]) that projects to the same approximate location as LC22. While we have yet to generate specific split-GAL4 drivers for this additional LPLC cell type and therefore did not further characterize these neurons here, their overlap with LC22 terminals was directly confirmed by co-labeling of single cells of both types in the same specimen (Figure 3—figure supplement 1). (G–I) LC10 neurons project to the large subunit of the AOTu. Overlays (generated as described above) showing cell shapes (G) and presynaptic sites (H) of LC10 cells labeled by two different split-GAL4 driver lines. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses neuroanatomical features and projections of specific neurons without addressing or relating to cognitive processes or cultural differences in cognition.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 02-fold), Fe (6. 41-fold, 7. 52-fold) and Mn (4. 57-fold, 5. 21-fold) in shoot and root and plant antioxidant enzymes i. e. glutathione S-transferase (7. 51-fold), peroxidase (5. 21-fold) and catalase (5. 01-fold) respectively. Our results revealed that inoculation of agricultural crops with PGPR is a very useful approach to increase the plant growth. The ACC (1-aminocyclopropane-1-carboxylate) enrichment technique is an efficient approach to select promising PGPR. The PGPR containing dual abilities i. e. both ACC-deaminase and nitrogen fixing ability are more effective than PGPR possessing either ACC-deaminase or nitrogen fixing activity alone for growth promotion of crops. Keywords: -deaminase, Ethylene and auxin, wheat 1. microorganisms are vital to agro-ecosystem health via their roles in organic matter decomposition, nutrient cycling and their associations with other organisms and plants i. e. symbiosis (Hassan et al. , 2013a; Glick, 2012). In agricultural soils, microorganisms are known to influence profoundly the status of soil fertility and health, and hence crop production (Ahmed and Kibret, 2014; Hassan et al. , 2013a). Different bacterial genera are vital components of soils, as it has been estimated that 80–90% of the processes in soil are reactions mediated by soil microbes (Hassan and David, 2014). They are involved in various biotic activities of the soil ecosystem to make it dynamic for nutrient turn over and ultimately sustainable crop production (Ahmed and Kibret, 2014). The large active groups of bacteria that inhabiting the rhizosphere benefit the crop plants in several ways, collectively called as plant growth promoting rhizobacteria (PGPR) (Hassan et al. , 2014a). Interactions between plants and microorganisms in the rhizosphere can clearly affect growth and development of the crop plants and yield (Hassan et al. , 2014a; Ahmed and Kibret, 2014). [SEP] Justify:

Output: The text corroborates the claim by discussing the benefits of plant growth promoting rhizobacteria (PGPR) in enhancing plant growth, specifically mentioning the nitrogen-fixing ability of PGPR as an effective mechanism for growth promotion. It emphasizes that PGPR with both ACC-deaminase and nitrogen-fixing capabilities are particularly effective in promoting crop growth, which supports the assertion that rhizobacteria play a significant role in enhancing plant growth through nitrogen fixation.


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: In such plots, each document is depicted as a point within a space that initially has as many dimensions as there are words or terms in the vocabulary. The LSA technique simplifies this space by uncovering the latent themes within the documents. This simplified, lower-dimensional space can then be displayed in a two or three-dimensional scatter plot. Here, each point represents a document, differentiated by color or label based on the topics it covers. LSA plots are beneficial for examining the interplay between documents and topics within a corpus, helping to spot clusters of similar documents. They’re also instrumental in tracing how topics have evolved by comparing LSA plots from different periods. While LSA effectively reduces text data to a more manageable form, it does have drawbacks. It assumes a bag-of-words model, ignoring word order, and may miss some nuanced meanings crucial for certain NLP tasks. Despite these limitations, LSA is still a common choice for dimensionality reduction and visualization in NLP. 4. 5 initial phase of our analysis involved preprocessing textual data, specifically customer reviews. We utilized the Encoder (USE) to convert these reviews into high-dimensional vector embeddings. This process transforms each review into a dense representation, capturing its semantic meaning effectively. The transformation can be represented as: where each review r is mapped to a vector in a continuous semantic space. The resulting embeddings for both training and test datasets are then reshaped and compiled into arrays, denoted as X train and X test, respectively. Our predictive model is constructed using TensorFlow’s Keras API, employing a sequential neural network architecture. The model is designed for binary classification and includes the following layers: A dense layer with 256 neurons and ReLU activation, processing the input embeddings. A dropout layer with a dropout rate of 0. 25, to prevent overfitting. A second dense layer with 128 neurons, again using ReLU activation. Another dropout layer with the same dropout rate to further regularize the model. The output layer with a single neuron and sigmoid activation function, suitable for binary classification tasks. The model is compiled with a binary cross-entropy loss function, and the Adam optimizer with a learning rate of 0. 001, an appropriate choice for this type of problem. The model undergoes training for 20 epochs with a batch size of 16, and a validation split of 20 is used. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of the LSA technique and the predictive model used. It notes that while LSA is useful for examining document similarities and topic evolution, it assumes a bag-of-words model which ignores nuances in word order and may miss crucial meanings. This indicates that AI's predictions based on these methods are not perfect and may lack the required accuracy due to inherent assumptions and limitations in the analysis methods discussed.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: LC10 neurons showed two distinct general projection paths with axons entering the large subunit of the AOTu from both dorsally and ventrally (magenta cells) or only from ventrally (green cells), in agreement with previous findings suggesting the existence of LC10 subtypes (Otsuna and Ito, 2006). (I) Anti-Brp reference pattern alone. The LC10 terminals are in a distinct large subcompartment (white bracket) of the AOTu. The AOTu also includes smaller subunits adjacent to this LC10 target region. Scale bars represent 30 µm (A,G,I), 20 µm (B,E) or 50 µm (D). Figure 3—figure supplement 1 Download asset Open asset A second type of columnar VPN projects to the LC22 target region. Main image and inset show two views along approximately orthogonal axes of MCFO labeled LC22 (green) and LPLC4 (magenta) cells generated using a GAL4 driver line (R11C10) with expression in both cell types. The layer pattern of LPLC4 dendrites is different from both LPLC1 and LPLC2 (see Figure 5 and Figure 7—figure supplement 2). Images show reoriented substack projection views generated using Vaa3D. Scale bar represents 20 µm. Figure 3—figure supplement 2 Download asset Open asset Presynaptic marker expression in individual LC cell types. Single confocal sections are shown. Images were rotated to show similar views. The anti-Brp reference pattern (grey) and the presynaptic marker (syt-smHA, magenta) are shown. The white appearance of the LC12 glomerulus is due to cross-talk between different labeling channels that occurred in some samples (see Materials and methods). Genotypes are the same as in the images used for the overlays in Figure 3. Many LC types also show some syt-smHA labeling in the lobula; examples of this are shown in Figure 5—figure supplement 2 and additional cases included in Supplementary file 1A. Scale bar represents 20 µm. Figure 4 with 1 supplement Download asset Open asset Multicolor stochastic labeling reveals differences in the arrangement of the terminal arbors of different LC types within their target glomerulus. (A–C) LC16. [SEP] Justify:

Output: The text describes various neural structures and their projections without addressing or implying any differences or similarities in cognitive processes across cultures. Therefore, it is irrelevant to the claim about cognitive processes.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: The PGPR can stimulate plant growth indirectly by inhibiting other deleterious microbes, root pathogens, biological control of root crop diseases, by production of antibiotics and siderophores (Ahmed and Kibret, 2014; Hayat et al. , 2010). Directly, PGPR provide the plant with a compound that is synthesized by the bacterium or facilitating the uptake of the nutrients (Ahmed and Kibret, 2014; Glick, 2012). Therefore, inoculation of agricultural crops with PGPR result in multiple positive effects on plant e. g. increases in the plant growth and vigor, increment in the chlorophyll, protein, proline and antioxidant enzymes activity and overall biomass of the host plant (Glick, 2012; Hayat et al. , 2010). Currently, the biological approaches for improving crop production are gaining strong attention among the agricultural scientists and researchers following integrated plant nutrient management system. The PGPR synthesize different phytohormones, including auxins, cytokinins, and gibberellins, which can enhance various stages of plant growth; and synthesize enzymes, including phosphatase, catalase, that can modulate plant growth and development, and strengthen their immune system (Glick, 2012). The secretion of a wide range of chemical compounds also modifies the chemical and physical properties of the soil (Ahmed and Kibret, 2014; Glick, 2012). Phytohormone auxin, (indole-3-acetic acid/IAA) is a class of phytohormones which are involved in the regulation of growth and development throughout the life cycle of plants (Ahmed and Kibret, 2014; Etesami et al. , 2014). Similarly, Ethyleneis also a vital phytohormone, which plays significant roles in the germination and growth and development of the seeds and crop plants (Bhattacharyya and Jha, 2012). Ethylene, produced endogenously by approximately all plants, however, apart from being a plant growth regulator, in excessive amount, ethylene has also been established as a stress hormone (Bhattacharyya and Jha, 2012). Several PGPR are actively involved in the synthesis of auxins in pure culture as well as in soil, and hence can play significant roles in the growth and development of crop plants (Glick, 2012). [SEP] Justify:

Output: The text corroborates the claim by describing how plant growth-promoting rhizobacteria (PGPR) can enhance plant growth through various mechanisms, including the production of phytohormones that stimulate growth and the improvement of nutrient uptake. Although the specific mention of fixing atmospheric nitrogen is not present, it indicates that PGPR contribute positively to plant growth, aligning with the broader theme of rhizobacteria's beneficial effects.


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: This validation split is crucial for monitoring the model’s performance and ensuring it does not overfit the training data. The training process involves shuffling the data for each epoch to maintain the randomness and independence of batches. Post-training, the model’s performance is evaluated on the test set. The evaluation metrics include loss and accuracy, which provide insights into the generalization ability of the model. The results are as follows: (0. 5371800661087036, 0. 8009422421455383) Furthermore, the training and validation loss and accuracy are plotted against epochs. These plots are crucial for visualizing the model’s learning trajectory and identifying any signs of overfitting or underfitting. 5 ConclusionsThe research presented in this paper aimed to develop a robust model for sentiment analysis, leveraging the capabilities of neural networks and natural language processing. Through the application of the Encoder (USE), we transformed textual data - specifically, customer reviews - into dense vector representations. This approach enabled us to capture the nuanced semantic content of the text, providing a rich foundation for our predictive modeling. Our model, constructed using TensorFlow’s Keras API, represents a sequential neural network specifically tailored for binary classification tasks. The architecture, comprising dense layers interspersed with dropout layers, was strategically designed to balance complexity and generalizability. The dense layers, equipped with ReLU activation functions, were pivotal in capturing the non-linear relationships within the data. In contrast, the dropout layers played a critical role in mitigating overfitting, a common pitfall in deep learning models. The training process, conducted over 20 epochs with a batch size of 16, was carefully monitored using a validation split. This approach not only provided insights into the model’s learning trajectory but also ensured that our model generalized well to unseen data, rather than merely memorizing the training dataset. The shuffle mechanism in the training process further contributed to the robustness of the model by preventing any order-specific biases in the learning process. Upon evaluation, the model demonstrated promising results, as indicated by the test loss and accuracy metrics. These metrics not only reflect the model’s performance in terms of prediction accuracy but also its ability to generalize to new, unseen data - a crucial aspect of any predictive model. However, it is important to acknowledge that these results are preliminary and subject to the limitations of the dataset and experimental setup. [SEP] Justify:

Output: The text contrasts the claim by outlining the limitations of AI models in predicting outcomes. It indicates that while the model shows promising results and generalizes well to unseen data, these results are preliminary and subject to the limitations of the dataset and experimental setup. This highlights that AI does not achieve perfect accuracy in predictions, contradicting the claim.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: (A) Position of the dendrites of three LC16 cells in the lobula in a layer cross-section view. Each cell occupies a distinct position along the long (DV) and short (AP) axes of the lobula (dotted white lines were added to facilitate comparison of the positions of different cells). By contrast, within the target glomerulus (B,C; two roughly orthogonal views are shown) the arbors of the same cells are intermingled without an obvious correlation to the retinotopic pattern in the lobula. Most other LC types appeared similar to LC16; additional examples are shown in Figure 4—figure supplement 1. (D–G) LC9. Most LC9 cells have presynaptic arbors that spread through more than one half of the glomerulus but do not fill it completely (F,G). Comparison of the approximate positions of arbors of individual LC9 cells in the glomerulus (F,G) with the AP positions of their dendrites in the lobula (D,E) suggests some retinotopy within the glomerulus, though with very low spatial resolution: For example, out of 57 examined LC9 cells, all of the cells (27/27) with axon terminals in the anterior-ventral tip of the glomerulus (such as the green cell in [F] and both cells in [G]) also had dendrites in the posterior half of the lobula while this was the case for only a few of the remaining LC9 cells (6/30). White dotted lines in (F) and (G) indicate the approximate boundaries of the LC9 glomerulus. (H–M) LC10. The relative order of LC10a (H–J) and LC10d (K–M) terminals along the DV axis of the AOTu (shown in two orthogonal views, I,J for LC10a and L,M for LC10d) matches the order along the AP axis of the lobula (H for LC10a and K for LC10d). Individual cells were labeled using MCFO. Reconstructed views of reoriented subtstacks generated in Vaa3D are shown. For both LC10a and LC10d, similar results were observed for LC10 cells from five optic lobes, each with at least three labeled cells. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the dendritic and axonal structures of certain neural cells in the lobula and glomerulus, with no mention of cognitive processes or cultural differences. The focus is on anatomical observations rather than cognitive theories or cross-cultural comparisons.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Alike, the PGPR containing ACC (1-aminocyclopropane-1-carboxylate) deaminase activity is helpful in lowering the ethylene levels by converting ACC into NH3 and α-ketobutyrate in plants, and support the plants for vigorous growth and development (Ahmed and Kibret, 2014). Wheat (Triticum spp. ) is one of the most important cereal crops in the world (Schalchli et al. , 2012). Globally, it is the most important human staple food grain and ranks second in total production as a cereal crop after maize (2006). In order to meet the demands of the increasing world,s population, the use of chemical fertilizers increased significantly (2006). It has been estimated that the total annual fertilizer use has risen from 37 M t of N, P and K in 1961-65 to 161 M t in 2005-09 (2009). The excessive use of chemical fertilizers, drastically increase the pollution in both natural and man made ecosystems (Hassan et al. , 2014b). Therefore, it is important to find out other self-propagating and eco-friendly sources, which can replace the chemical fertilizers entirely or partially (Hassan et al. , 2014b). In this regard, the use of PGPR as a bio-fertilizer is a viable approach. Keeping in view all these challenges, the current study was conducted with the objectives: (1) to isolate and screen the most effective multi-traits PGPR strains for wheat (2) to examine the physical, chemical and enzymatic responses of wheat plant to three different rhizobacterial strains, strain 1: containing ACC-deaminase activity (WACC1, WACC2), strain 2: having nitrogen fixing ability (WRN1, WRN5) and strain 3: having both ACC-deaminase activity and nitrogen fixing ability (WAN1, WAN2). 2. Materials and Methods 2. 1. Physico-chemical analysis The soil was collected at 0-5 cm depth with the help of an augur. The samples were air-dried and passed through 2-mm sieve for further use in the incubation study. [SEP] Justify:

Output: The text corroborates the claim by indicating that one of the examined rhizobacterial strains has nitrogen-fixing ability, which is a key function of rhizobacteria that enhances plant growth. Specifically, it mentions a strain (WRN1, WRN5) that possesses nitrogen-fixing ability, thus supporting the idea that rhizobacteria can enhance plant growth through this mechanism.


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 graphical analysis of training and validation losses and accuracies provided additional layers of insight. The trends observed in these plots are critical in diagnosing issues such as overfitting or underfitting and in understanding the learning dynamics of the model across epochs. They also serve as a visual affirmation of the model’s learning stability and convergence behavior. This research contributes to the burgeoning field of sentiment analysis by demonstrating the effectiveness of dense neural networks in handling complex, high-dimensional data derived from natural language. The successful application of the Encoder in transforming textual data into a format amenable for neural network processing underscores the synergy between state-of-the-art NLP techniques and neural network architectures. However, this study is not without its limitations. The scope of the data and the specific architecture of the neural network model present avenues for further exploration. Future work could involve experimenting with different neural network architectures, such as recurrent neural networks or transformers, which might be more adept at capturing sequential dependencies in text. In addition, investigating the effects of different embedding techniques, or incorporating additional features, could provide more comprehensive insights. Moreover, the application of the model to a more diverse set of data sources would enhance its robustness and applicability to real-world scenarios. In conclusion, our research marks a significant step in sentiment analysis, providing a framework that can be built upon and refined in future studies. The integration of advanced NLP techniques with neural networks holds great promise for the field, and continued exploration in this domain is essential for the development of more sophisticated and nuanced sentiment analysis tools. DeclarationsCompeting interests: The authors declare no competing interests. 260. LeCun, Y. , Bengio, Y. , Hinton, G. (2015). Deep learning. Nature, 521(7553), 436444. Liu, B. (2012). . Publishers. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. The MIT Press. Pang, B. , Lee, L. (2008). . Now Publishers. Russell, S. , Norvig, P. (2010). Artificial Intelligence: A Modern Approach. PrenticeHall. Tibshirani, R. , Friedman, J. (2009). of Statistical Learning: DataMining, Inference, and Prediction. Springer. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and uncertainties in predicting outcomes using AI. It emphasizes that while the graphical analysis of training losses and accuracies reveals insights into model behavior, it also highlights limitations such as potential issues with overfitting and the need for future exploration with diverse data and alternative architectures. This suggests that AI predictions are not infallible and that perfect accuracy cannot be guaranteed.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: Analyses of MCFO-labeled LC10b (36 cells from 18 brains) and LC10c (33 cells from 17 brains) single cells also showed an approximate correspondence between AP positions of dendrites in the lobula and DV positions of axonal arbors in the AOTu. LC10b cells also showed considerable variation in their lateral-medial spread within the medial zone of the AOTu but further analyses will be required to explore possible correlations between these differences and arbor positions in the lobula. Scale bars represent 20 µm (A,D,E,H,K) or 10 µm (C,F,G,I,L). Figure 4—figure supplement 1 Download asset Open asset Terminal arbor arrangements of additional LC cell types. (A–C) LC6, (D–F) LC11, (G–I) LC13, (J–L) LC12, (M–O) LC18, (P–R) LPLC2. Substantial overlap of the arbors of co-labeled single cells in their target regions was also observed for the remaining LC cell types (at least two brains with two or more labeled cells were examined for each cell type). While there are occasional differences between the spread of single cells of the same type within their target glomerulus (e. g. the LC6 cells in (B) do not all extend to the lateral tip of the glomerulus), we did not find obvious correlations between such differences and these cells’ retinotopic positions in the lobula. Lobula cross-section views and two orthogonal views of the target glomerulus are displayed as in Figure 4. Scale bars represent 20 µm (A) or 10 µm (C). Figure 5 with 2 supplements Download asset Open asset Layer specific arborizations of LC neurons in the lobula. (A) Anti-Brp neuropil marker shows bands of different intensity in the lobula that can serve as approximate markers of layer boundaries. The image is a maximum intensity projection through 10 adjacent sections (0. 38 µm spacing) of the reference channel of the standard brain used for alignments. Approximate layer boundaries are indicated. Layer boundaries were defined by the positions of known cell types and closely match the anti-Brp pattern (see Figure 5—figure supplement 1). [SEP] Justify:

Output: The text presents a detailed analysis of neural structures and their arrangements within specific brain regions, focusing on the variability and organization of neuronal arbors. It does not address cognitive processes or make any claims regarding their consistency across cultures, thereby being irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Soil texture, pH, EC (electrical conductivity), available P, N, extractable K and organic matter (OM) and saturation percentage were determined by the methods of Hassan, 2013, Hassan et al. , (2013 b, c, d) and Hassan et al. , (2014 c, d, e). The soil was sandy loam (sand 60. 3%, clay 25. 5% and silt 14. 2%) in texture, and alkaline in nature with saturation percentage 41. 5%, pH 7. 98, EC 1. 71, OM 0. 41%, available P, N and K 8. 81, 3. 67 and 423 mg kg-1respectively. 2. 2. Isolation of PGPR For isolation of bacterial strains, gently rhizosphere soil was collected from tomato crop. Rhizobacteria were isolated by dilution plate technique (Wollum-II, 1982) using Dworkin and Foster salt minimal media (Dworkin and Foster, 1958) (DF). For PGPR containing ACC-deaminase, ACC was the sole nitrogen source. Whereas, modified mannitol agar media was used to isolate PGPR possessing nitrogen fixing activity (Enrichment Technique). 2. 3. Isolation of rhizobacteria containing dual abilities Isolated rhizobacteria were further grown on both (DF salt minimal and modified mannitol agar) media to isolate the rhizobacteria containing dual abilities i. e. nitrogen fixing and ACC-deaminase activity. The isolates having ACC-deaminase activity was grown on modified mannitol agar media used for N fixing isolates and the rhizobacteria possessing N fixing activity was grown on media used for isolates having ACC-deaminase activity. The isolates WAN1 and WAN2 grew on both media and proved that having both abilities. 2. 4. Preparation of inocula Autoclaved 250 ml flasks with DF salt minimal media were used for the preparation of inocula, for the rhizobacterial isolates containing ACC-deaminase activity and modified mannitol agar media for the rhizobacterial isolates having nitrogen fixing activity. The autoclaved flasks were incubated at 28 ± 1°C for 48 hours in the orbital shaking incubator at 100 rpm. [SEP] Justify:

Output: The text supports the claim by describing the isolation of rhizobacteria that possess nitrogen-fixing activity. It mentions that modified mannitol agar media was specifically used to isolate rhizobacteria capable of fixing nitrogen, thereby corroborating the notion that rhizobacteria play a role in enhancing plant growth through this mechanism.


Input: Task: contrast the claim using the given text. Claim: The majority of digital transactions today use cryptographic techniques to ensure the security of sensitive information. [SEP] Text: Nevertheless, when this communication happens in the digital world through the Internet, where people do not meet or even know each other, such a protection is more challenging to obtain, and additional digital tools are needed in order to defend users. In this thesis we investigate how to enforce the honest behavior of parties involved in a digital interaction over the Internet. In particular, we consider two emerging paradigms in this setting: Could computing and E-commerce. By using a Cloud service, anyone (a Client) can outsource data to a remote server (the Cloud), and later on ask the Cloud to perform operations on them. This new paradigm allows for huge potential savings in terms of time, computing power and other kind of infrastructural costs, since it is possible to access to pay-per-use services upon request. It is clear that a computing paradigm like this is very attractive but, at the same time, trusting the Cloud can be problematic for various reasons. Two main security concerns that have been recently given attention by the research community are those about the privacy and authenticity of the data stored and processed in untrusted environments. Intuitively, for privacy a Client does not want the server to learn any information about the outsourced data. For authenticity, the Client instead wants to be sure that the Cloud computed correctly on the outsourced data. In this thesis we focus on this second problem, advancing the study of homomorphic authenticators. In homomorphic authenticators a outsources authenticated data to the Cloud. Later on, a third entity (the Verifier) can ask the Cloud to compute a function f over the Client’s outsourced data. Using a special procedure, the Cloud can provide the Verifier with an authenticator for the output of the function, which allows the Verifier to check the validity of the computation queried. The contribution of this thesis addresses three different aspects of homomorphic authenticators: definitions, efficiency and functionalities. First, it introduces a new security model which is stronger and easier to deal with compared with the existing one, along with two compilers which allow one to go from the old model to the new one. Second, it provides the first linearly homomorphic signature scheme whose verification keys have size sublinear in that of the outsourced dataset. Third, it formalizes the notion of homomorphic authenticators for functions which take inputs authenticated using different keys, providing concrete constructions both in the case of private and public verification. [SEP] Justify:

Output: The text contrasts the claim by highlighting the challenges in ensuring protection for digital communication over the Internet, indicating that additional tools are required to defend users. It mentions specific security concerns related to privacy and authenticity of data in untrusted environments, which suggests that while cryptography may be present, it alone is insufficient to ensure security without addressing these complexities.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: Lo5 was divided into two sublayers based on the anti-Brp pattern. Further subdivisions of strata based on the positions of arbors of different cell types would be possible but were not applied here. (B,C) Layer patterns of LC6 (B) and LC12 (C). Split-GAL4 expression of a membrane marker is shown in green. Images were aligned to a standard brain using the anti-Brp pattern (shown in grey). Images are maximum intensity projections through the same series of sections of brains aligned to the same template as in (A). Approximate layer boundaries are marked with white lines. (D) Layer patterns of the remaining LC cell types (except LC10 neurons). Projections were generated as in (B,C) but are shown without the anti-Brp pattern. All layers, but only a portion of the lobula is shown. Schematics in (B–F) indicate innervated layers as filled circles; black circles represent more extensive arborizations than grey circles. Note that these simplified schematics do not capture some details of the layer patterns (such as sublayer patterns). An additional description of layer patterns can be found in Supplementary file 1A. (E) Single cell layer patterns are consistent with layer patterns seen at the population level. LC4 and LC15 are shown as examples. Additional single cell images can be found in Figure 7—figure supplement 1. (F) Layer patterns of LC10 subtypes. LC10b and LC10d cells have similar layer patterns but differ in other aspects such as arbor size (LC10b arbors in the lobula are larger). Additional examples of MCFO labeled LC10 cells of different subtypes can be found in Figure 7—figure supplement 1 and Figure 10—figure supplement 2. Images in (E,F) were manually matched to the layer markers using the anti-Brp pattern. The scale of some images was slightly adjusted to compensate for varying depth of the lobula. Scale bars represent 20 µm (A, also applies to B and C) or 10 µm (D,E; scale bar in E also applies to F). Figure 5—figure supplement 1 Download asset Open asset Layer positions of arbors of known cell types. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim as it discusses the anatomical features and layer patterns of neurons in the lobula without addressing any cognitive processes or cultural aspects. There is no mention or implication regarding cross-cultural cognitive similarities or differences.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Experimental design Two PGPR isolates (WACC1 and WACC2) containing ACC-deaminase activity, two PGPR isolates (WRN1 and WRN5) having nitrogen fixing activity and two PGPR isolates (WAN1 and WAN2) possessing both ACC-deaminase and nitrogen fixing activity were selected for incubation experiment. The inoculum for trial was prepared by growing the selected PGPR isolates. For inoculation of treatments, germinated seeds (variety Uqab-2000) were dipped in broth having selected ACC-deaminase and/or nitrogen fixing isolates. For control treatments, the wheat seeds were dipped in sterilized flasks containing 0. 03 M MgSO4. Four inoculated seeds were sown in each pot having 500 g soil/pot. Sterilized ½ strength nitrogen free Hoagland solution was used to provide nutrients to growing seedling. There were four replications for each treatment. Pots were arranged in complete randomized design (CRD) in a growth room under axenic conditions. After 28 days of germination, wheat plants were harvested and parameters regarding root and shoot length, seedling fresh and dry weight, chlorophyll a, b and carotenoid contents, macro and micro nutrients in plant shoot and root and antioxidant enzymes glutathione S-transferase, peroxidase and catalase activities were recorded. 2. 11. Statistical analysis The data were statistically analyzed by Statistix 8. 1 statistical package (Statistix, USA). Parametric statistics of ANOVA analysis was carried out to estimate the effect of rhizobacterial isolate inoculation on plant growth. Mean separations were achieved using a least significant difference (LSD) test at p < 0. 05. 3. Results 3. 1. Root and shoot length The inoculation of wheat seedlings with rhizobacterial isolates containing nitrogen fixing and/or ACC-deaminase activity increased the root and shoot length as compared to uninoculated control. The root and shoot length ranged from 1. 51-fold to 3. 51-fold and 1. 40 to 3. 22 fold under normal growth conditions (Figure 1). The isolate WAN1 showed maximum increase in the root and shoot length that was 3. 51-fold and 3. 22-fold respectively as compared to uninoculated control. [SEP] Justify:

Output: The text corroborates the claim by detailing an experimental design that demonstrates the effect of PGPR (Plant Growth-Promoting Rhizobacteria) isolates with nitrogen-fixing activity on the growth of wheat plants. It states that the inoculation of wheat seedlings with these rhizobacterial isolates led to an increase in root and shoot length compared to the uninoculated control. Specifically, it highlights that isolates containing nitrogen-fixing activity contributed to significant improvements in plant growth metrics, thereby supporting the claim that rhizobacteria can enhance plant growth through nitrogen fixation.


Input: Task: contrast the claim using the given text. Claim: Quantum computing has the potential to break traditional encryption methods by solving complex problems much faster. [SEP] Text: This is difficult to ascertain as (i) no times (or memory requirements) are quoted, (ii) no 'traditional method', that can actually align the required number of sequences without human intervention, is used and (iii) the results are *not* repeatable. and UPP I have repeatedly (UPP five times, PASTA twice) aligned the same set of (small in number and short) sequences and every time obtained a different alignment. In my example the alignment lengths varied by 7%, the sum-of-pairs scores of one alignment against another alignment varied from 96% to 97% and the total column scores from 23% to 40%. For 'repeatable' results I would consistently expect 100%. understand UPP/PASTA correctly, then there is a certain degree of randomness involved in the assignation of the backbone sequences. However, I did not see that there was (can be? ) any random seed specified on the command-line. If this is so, then the alignments have to be repeated several times and the results have to be somehow combined/averaged. It is well known that -INS-i is not thread-safe. SATe/PASTA/UPP use -INS-i (indeed it is my opinion, that much of the quality of SATe/PASTA/UPP is due to this fact, see below) so a 'repeatable' analysis should be run such that -INS-i is run single-threaded (or by setting the MAFFT --threadit flag to 0). Action: - Quote run-times and memory consumption. - Guarantee that alignments are repeatable. - If this cannot be done then repeat alignments and average. [8] As mentioned above, SATe/PASTA/UPP use -INS-i, L-INS-i is a high-quality consistency aligner and is not. It would be only fair to compare like with like and either (i) use as the base aligner in UPP (PASTA can do this, can UPP? ) or (ii) use L-INS-i as the 'traditional method'. L-INS-i would definitely have problems aligning 55k sequences, but since in the current analysis sequences are broken up into 1,269 FASTA files, this should be feasible. [SEP] Justify:

Output: The text contrasts the claim by discussing the lack of repeatability and reliability in the methods described, suggesting that traditional methods cannot be effectively evaluated or compared without controlled conditions. It raises concerns about the randomness and inconsistency in alignment results, which implies that the effectiveness of any method, whether traditional or quantum, cannot be assured if cannot be replicated, ultimately questioning the validity of the assertion regarding quantum computing's superiority over traditional methods.


Input: Task: pass the claim using the given text. Claim: All cognitive processes are the same across cultures. [SEP] Text: Lobula terminals of MCFO labeled T5, Tm9, Tm4, Tm3 and Tm20 cells (Fischbach and Dittrich, 1989) are shown. Anti-Brp pattern is in grey. Layer boundaries are marked with white lines. Golgi studies (Fischbach and Dittrich, 1989) have described T5 and Tm9 in Lo1, Tm4 and Tm3 proximal terminals in Lo4 near the boundary of Lo5, and Tm20 terminals in Lo5 near the boundary of Lo6. Tm4 and Tm3 also have apparent presynaptic varicosities in Lo2 (both) and Lo1 (Tm4). These cell types therefore can be used to identify the approximate boundaries of all six lobula strata. Further division of Lo5 into two sublayers is suggested by the anti-Brp pattern and supported by the differential presence of arbors of several neuronal cell types in these sublayers (compare for example, LC20 and LC25 in Figure 5D). Images show reoriented substack projection views generated using Vaa3D. Scale bar represents 10 µm. Figure 5—figure supplement 2 Download asset Open asset Potential presynaptic sites of LC neurons in the lobula. In addition to the target glomerulus in the central brain (see Figure 3 and Figure 3—figure supplement 2), split-GAL4 driven pJFRC51-3XUAS-IVS-syt::smHA in su(Hw)attP1 expression also resulted in labeling in the lobula in several LC cell types. Single confocal sections with anti-Brp in grey, syt-smHA in magenta and/or a membrane marker (pJFRC225-5XUAS-IVS-myr::smFLAG in VK00005) in green are shown for each cell type as indicated in (A). Although the interpretation of such fainter syt-smHA labeling patterns is not always straightforward, they strongly suggest that at least some LC neurons (including cell types not shown in this Figure) also have presynaptic sites within the lobula. Additional details on syt-HA labeling in the lobula are provided in Supplementary file 1A. [SEP] Justify:

Output: The text is irrelevant to the claim as it provides detailed descriptions of neuronal cell types and their locations in the lobula, along with references to specific studies and figures. It does not address cognitive processes or their cultural differences, thus it does not support or contradict the claim.


Input: Task: corroborate the claim using the given text. Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Conversely, WAN2 was the next effective isolate in promoting root and shoot length (3. 1-fold and 2. 91-fold) as compared to uninoculated control. The other isolates namely, WRN1, WRN5, WACC1 and WACC2 caused 2. 51-fold, 2. 21-fold, 2. 22-fold, 1. 91-fold, 1. 81-fold, 1. 61-fold and 1. 51-fold and 1. 40-fold increase in root and shoot length as compared to uninoculated control. Figure 1. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on root and shoot length of wheat. Different letters (a-g) on bars indicate significant differences of mean values for root length. Bars represent standard errors. 3. 2. Fresh and dry weight of seedlings The effect of inoculation with either ACC-deaminase and/or nitrogen fixing activity possessing rhizobacteria on the fresh and dry weight of seedling is shown in Figure 2. Data showed that fresh and dry weight of seedlings increased significantly (P < 0. 05) as a result of inoculation, ranged from 1. 51 to 3. 41 fold and 1. 60 to 3. 91 fold. All rhizobacterial isolates caused significant (P < 0. 05) increase in the fresh weight and dry of seedlings. Nevertheless the WAN1 was the most efficient isolate, caused an increase of 3. 41-fold and 3. 91-fold. The WAN2 was the second best isolate, caused an increase of 3. 11-fold and 3. 41-fold. The next effective isolates were WRN1, WRN5, WACC1 and WACC2 that resulted in about 2. 61-fold and 2. 92-fold, 2. 41-fold and 2. 61-fold, 1. 81-fold and 2. 21-fold and 1. 51-fold and 1. 60-fold increase respectively. Figure 2. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on seedling fresh and dry weight of wheat. Different letters (a-g) on bars indicate significant differences of mean values for seedling fresh weight. Bars represent standard errors. 3. 3. [SEP] Justify:

Output: The text supports the claim by detailing the effectiveness of various nitrogen-fixing rhizobacteria in promoting plant growth, specifically noting significant increases in root and shoot length as well as fresh and dry weight of seedlings due to inoculation. It mentions that all tested rhizobacterial isolates caused significant increases in growth metrics, indicating a positive relationship between rhizobacteria and plant growth.