Input: Task: contrast the claim using the given text. Claim: Eco-tourism can completely replace conventional tourism worldwide. [SEP] Text: This highlights the need for a local level planning, which is endorsed by all tourism agents and has an accessible economic and environmental load capacity and balance. Promoting planned tourist areas will be possible with the support of government policies, aimed at progressively implementing clean energies, promoting the growth of undeveloped areas and including citizens in the evolution of tourism economies. Our initial hypothesis is confirmed and the path towards the logistic change of sustainable development is aimed at the protection and conservation of natural and cultural heritage. declarationsI give thanks to the InetchOpen editorial for inviting me to participate write a book, directed and supervised by Rehman Khan, on tourism that reflects the current situation of the sector in today’s world and during this period marked by the COVID-19 pandemic. 1. Naciones Unidas. El ecoturismo como elemento clave para erradicar la pobreza y proteger el medioambiente. Asamblea general de las Naciones Unidas. PRNo: PR13001. 2012. Available at: https//www. unwto. org/es/archive/press-release/2013-01-09/asamblea-general-de-las-naciones-unidas-el-ecoturismo-como elemento-clave-p2. Báez A, Acuña A. Guía para las mejores prácticas de ecoturismo en áreas protegidas. Comisión Nacional para el desarrollo de los pueblos indígenas. México CDI, 2003, pg. 1593. GM. Ecoturismo instrumento de Desarrollo sostenible [Tesis] Medellín: Universidad de Antioquía; 20064. Organización Mundial del Turismo (OMT). Agenda para planificadores locales. Turismo sostenible y gestión municipal. Publicación de Turismo y medioambiente. Edición para América Latina y el 19995. Academic. Modelo FAS de la Organización Mundial del Turismo. Wikipedia Español; 2000. Available at: Martínez Quintana V. [SEP] Justify:

Output: The text contrasts the claim by implying that eco-tourism cannot completely replace conventional tourism but must be integrated with local planning and government policies. It emphasizes the need for a planned approach to tourism development rather than suggesting a total replacement of conventional tourism.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: J. Neurosci. 41(18), 4100–4119. (2021). 603. (2021). 10. 1101/2020. 12. 02. 403477 (2021). 1910. 14599, (2019). Lakretz, Y. , Desbordes, T. , King, J. -R. Crabbé, B. , Oquab, M. & Dehaene, S. Can RNNs learn recursive nested subject-verb agreements? arXiv:2101. 02258 [cs], (2021). Hupkes, D. , Dankers, V. , Mul, M. & Bruni, E. Compositionality decomposed: How do neural networks generalise? . J. Artif. Intell. Res. 67, 757–795 (2020). 2008. 01766 [cs], (2021). Linzen, T. & Baroni, M. Syntactic structure from deep learning. Annu. Rev. Linguist. 7, 195–212 (2021). 5974. (2020). 1904. 09751 [cs], (2020). Wiseman, S. , Shieber, S. M. & Rush, A. M. Challenges in data-to-document generation. arXiv:1707. 08052 [cs], (2017). Thakur, N. , Reimers, N. , Ruckle, A. , Srivastava, A. , & Gurevych, I. BEIR: A heterogenous benchmark for zero-shot evaluation of information retrieval models. arXiv:2104. 08663 [cs], (2021). Raffel, C. , Shazeer, N. , Roberts, A. , Lee, K. , Narang, S. , Matena, M. , Zhou, Y. , Li, W. & Liu, P. J. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv:1910. 10683 [cs, stat], (2020). Krishna, K. , Roy, A. & Iyyer, M. Hurdles to progress in long-form question answering. arXiv:2103. 06332 [cs], (2021). Yamins, D. L. [SEP] Justify:

Output: The text does not provide any relevant information regarding the claim about advanced algorithms predicting emotions based on brain scans. Instead, it lists various research papers and their citations without addressing the specific claim at hand.


Input: Task: corroborate the claim using the given text. Claim: AI algorithms are used in diagnostics to analyze medical imaging more quickly. [SEP] Text: The F1-score balances precision and recall, providing a single effectiveness measure, and ROC curves with AUC values offer a comprehensive view of the model’s diagnostic capabilities across different classes (benign, malignant, normal). The confusion matrix visualizes the model’s performance, identifying areas for improvement, and the inclusion of error metrics like and RMSE further quantifies prediction accuracy and sensitivity to outliers. These combined metrics offer a thorough assessment of the model’s reliability and effectiveness in clinical applications, particularly in minimizing false positives and ensuring early detection in medical diagnosis. Experimentation & resultsThe experimentation with the EfficientNet-B7 model on the BUSI dataset demonstrated exceptional performance in classifying breast ultrasound images into benign, malignant, and normal categories. The model achieved high precision, recall, and F1-scores across all classes, with an overall accuracy of 99. 14%. Error metrics, including and RMSE, indicated minimal prediction errors, underscoring the model’s robustness. Different metricsIn this study, we employed a comprehensive set of evaluation metrics to assess the performance of the EfficientNet-B7 model on breast ultrasound image classification. These metrics included Precision, which indicates the proportion of correctly predicted positive observations to the total predicted positives, providing insight into the model’s accuracy regarding true positive identification, crucial for minimizing false positives in medical diagnoses. Recall, also known as sensitivity, measures the proportion of actual positives correctly identified by the model, essential in medical imaging to ensure all true cases, such as malignant tumors, are detected. The F1-Score, the harmonic mean of precision and recall, offers a balanced measure of both false positives and false negatives, particularly valuable for imbalanced datasets. Matrix presents a detailed breakdown of true positives, true negatives, false positives, and false negatives for each class, offering a clear visualization of the model’s performance across all categories. The ROC-AUC metric, with the ROC curve plotting the true positive rate against the false positive rate, provides a visual representation of the model’s diagnostic ability across various thresholds, with the AUC quantifying this ability, where values closer to 1 indicate superior performance. Additionally, such as Error (MAE), Error (MSE), and (RMSE) were used to provide quantitative measures of prediction accuracy and error magnitude [21, 22]. [SEP] Justify:

Output: The text corroborates the claim by detailing the use of the EfficientNet-B7 model in classifying breast ultrasound images. It describes how this AI model achieved high precision, recall, and F1-scores, indicating that AI algorithms can effectively analyze medical imaging. The emphasis on performance metrics and the model's success in diagnosing benign, malignant, and normal categories supports the idea that AI is utilized in diagnostics, thereby enhancing the speed and accuracy of medical imaging analysis.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4749003/v1This work is licensed under a CC BY 4. 0 LicenseStatus:Under ReviewVersion 1posted 10You are reading this latest preprint versionAbstractThe primary aim of this study is to investigate the factors that influence the behavioral intentions of Chinese green tourists within the realm of virtual tourism experiences, utilizing a combination of qualitative and quantitative research methods. To accomplish this, we initiated the study with grounded theory as our starting point, conducting 36 comprehensive interviews with Chinese green tourists. These interviews were meticulously coded and subjected to in-depth analysis, leading to the development of a robust research model outlining the factors influencing Chinese green tourists' behavioral intentions towards virtual tourism experiences. Subsequently, we conducted a quantitative analysis of 740 collected questionnaires concerning behavioral intentions towards virtual tourism experiences, subjecting each variable within the theoretical model to empirical testing. The results unequivocally demonstrate that Chinese green tourists' cognitive factors, perception factors, experience type factors, and social situation factors all wield significant influence over their behavioral intentions towards virtual tourism experiences. This study provides nuanced insights into factors shaping green tourists' decisions and highlights virtual tourism's growing importance in sustainable travel. It offers valuable guidance for researchers and industry practitioners aiming to enhance virtual tourism experiences for environmentally conscious Chinese tourists, contributing to the discourse on sustainable tourism and the potential for a more ecologically sustainable future. Biological sciences/Psychology/Human behaviourEarth and environmental sciences/Environmental social sciences/Psychology and behaviourChinese green touristsSustainable tourismVirtual experiencesBehavioral intentionsMixed methodsFiguresFigure 1Figure 21 IntroductionEnvironmental protection and sustainable development have emerged as global priorities, and the tourism industry is no exception. Green tourism strives to minimize the impact on the natural environment and cultural resources while promoting the sustainable development of the environment, economy, and society. -19 pandemic, during its most severe phase, imposed strict restrictions on mobility and significantly disrupted traditional field tourism activities1. As technology continues to advance, with developments such as 5G, artificial intelligence, augmented reality, and virtual reality, the transformation of the tourism industry into the realm of digital technology is an undeniable trend2. Virtual tourism aligns itself with the principles of sustainable development, emphasizing the preservation and promotion of the environment, society, and culture, all while placing a strong emphasis on the quality of the tourism experience. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities involved in virtual tourism experiences and the various factors that influence behavioral intentions of tourists. While it emphasizes sustainable tourism and outlines its benefits, it does not guarantee a positive experience for all travelers, highlighting that factors such as cognitive, perception, experience type, and social situations significantly influence tourists' behavioral intentions. Therefore, the inevitability of a guaranteed positive experience, as suggested in the claim, is not supported.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl. Acad. Sci. 111(23), 8619–8624. (2014). 458. (2016). 15. (2010). 1. 0 contributors. SciPy 1. 0: Fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272. (2020). 236. (2012). 2915. (2011). 2527. (2011). 10. 1073/pnas. 1612132113 (2016). 1392. (2011). 402. (2007). 362. (2014). 821. (2006). 76. (2013). 726. (2001). 4445. (2022). 6106. (2017). 809. (2018). 1456. (2008). 270. (2012). 2111. 14232 [cs, q-bio], (2021). Scott, M. et al. Reproducible brain-wide association studies require thousands of individuals. Nature (2022). 10. 1073/pnas. 1907367117 (2020). 10. 18653/v1/D19-1050. Reddy, A. J. & Wehbe, L. Syntactic representations in the human brain: Beyond effort-based metrics. preprint, Neuroscience (2020). Yamins, D. L. K. & DiCarlo, J. J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19(3), 356–365. (2016). 0307. (2020). 2004. 10151 [cs], (2020). Brown, T. B. , Mann, B. , Ryder, N. , Subbiah, M. , Kaplan, J. , Dhariwal, P. , Neelakantan, A. , Shyam, P. , Sastry, G. , Askell, A. , Agarwal, S. , Herbert-Voss, A. , Krueger, G. , Henighan, T. , Child, R. , Ramesh, A. , Ziegler, D. M. , Wu, J. , Winter, C. , Hesse, C. , Chen, M. , Sigler, E. , Litwin, M. [SEP] Justify:

Output: The text does not address the claim regarding advanced algorithms predicting emotions based on brain scans. Instead, it consists of a list of citations and references to various studies without discussion or mention of emotional prediction algorithms or brain scans.


Input: Task: corroborate the claim using the given text. Claim: AI algorithms are used in diagnostics to analyze medical imaging more quickly. [SEP] Text: Table 6 Summarizes the error metrics, including and RMSE, to assess prediction accuracy. Table 6 Error metrics summaryFull size tableObserved resultsThe results from the experimentation with the EfficientNet-B7 model on breast ultrasound image classification were outstanding. Precision values were exceptionally high, with 1. 000 for benign, 1. 0 for malignant, and 0. 952 for normal classes, resulting in a macro average precision of 0. 984 and a weighted average precision of 0. 992, demonstrating high accuracy in predicting true positives. Recall values were 0. 985 for benign, 1. 0 for malignant, and 1. 0 for normal, yielding a macro average of 0. 995 and a weighted average of 0. 991, reflecting the model’s excellent capability to identify all actual positive cases. The F1-scores were 0. 992 for benign, 1. 0 for malignant, and 0. 976 for normal, with a macro average of 0. 989 and a weighted average of 0. 992, indicating a strong balance between precision and recall. The model achieved an overall accuracy of 0. 991, showcasing robust performance across all classes. Error metrics were also favourable, with a Error (MAE) of 0. 017, Error (MSE) of 0. 034, and (RMSE) of 0. 185, indicating minimal prediction errors. The ROC-AUC scores were perfect, with 1. 00 for benign, malignant, and normal classes, highlighting the model’s excellent discriminative ability. These results demonstrate the high effectiveness of the EfficientNet-B7 model in classifying breast ultrasound images, with exceptional precision, recall, and overall accuracy, further confirmed by the minimal prediction errors indicated by the error metrics. Figure 9 displaying the classification results for benign, malignant, and normal cases, illustrating true positives, false negatives, and false positives. Figure 10 shows precision, recall, and F1-score for benign, malignant, and normal classes, including accuracy, macro average, and weighted average metrics. Figure 11 shows the true positive rate versus false positive rate for benign, malignant, and normal classes, with the area under the curve (AUC) values. Fig. 9Confusion matrixFull size image Fig. 10Classification report heatmapFull size image Fig. [SEP] Justify:

Output: The text corroborates the claim as it describes the performance of the EfficientNet-B7 model in classifying breast ultrasound images, highlighting its exceptional precision, recall, and overall accuracy in diagnostics. The mention of 'high effectiveness' and 'robust performance' of the model supports the notion that AI algorithms can analyze medical imaging accurately and efficiently.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: This philosophy closely resonates with the ideals held by green tourists. In summary, the application of virtual tourism expands the horizons of green consumption and provides an ideal response to the imperatives of sustainable development and green ecotourism. While virtual tourism encompasses various environmentally friendly modes, such as low-carbon tourism, water and energy conservation, and ecological preservation, the question remains: Can it truly gain widespread acceptance among green tourists? There exists a significant gap between the perceived reality of virtual tourism experiences and the authenticity of green tourism encounters3. Furthermore, the numerous uncertainties surrounding virtual tourism dampen the enthusiasm of green tourists for such experiences, thereby impacting their behavioral intentions to engage in virtual tourism4. The decision-making process of green tourists' experiential behavior is intricate and multifaceted. Currently, there is a dearth of research exploring the influencing mechanisms behind green tourists' attitudes and intentions in virtual tourism, and a systematic theoretical framework is conspicuously absent for explaining the factors influencing green tourists' behavioral intentions towards virtual tourism experiences. This study endeavors to bridge this gap by investigating the influencing factors on green tourists' behavioral intentions towards virtual tourism experiences, utilizing a mixed research approach that combines qualitative and quantitative methodologies. In the initial phase, we conducted in-depth interviews with 36 Chinese green tourists who expressed an interest in virtual tourism experiences. Using grounded theory as our qualitative research method, we meticulously coded and analyzed the data, culminating in the construction of a comprehensive model illustrating the influencing factors affecting green tourists' behavioral intentions towards virtual tourism experiences. Subsequently, in the second stage of our study, we designed and distributed relevant survey questionnaires to gather data from 740 Chinese green tourists. Employing quantitative research methods, we rigorously validated the model derived from grounded theory. This enabled us to delve deeply into the mechanisms governing tourists' experiential behavior characteristics in virtual tourism. In essence, this study aspires to provide a novel theoretical perspective that can inform the future transformation and integration of virtual tourism and green tourism activities. It seeks to shed light on the acceptance and feasibility of virtual tourism within the green tourism context, offering valuable insights for future endeavors in sustainable and eco-friendly travel. 2 Literature review 2. 1 Green tourism and virtual tourism In the 21st century, a global wave of green revolution has been unleashed, encompassing the realms of the green economy, green consumption, green marketing, and green GDP5. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while eco-tourism and virtual tourism aim for sustainability, there are uncertainties and gaps affecting green tourists' experiences. It mentions that 'there exists a significant gap between the perceived reality of virtual tourism experiences and the authenticity of green tourism encounters,' indicating that not all experiences in eco-tourism are guaranteed to be positive.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: , Gray, S. , Chess, B. , Clark, J. , Berner, C. , McCandlish, S. , Radford, A. , Sutskever, I. & Amodei, D. Language models are few-shot learners. arXiv:2005. 14165 [cs], (2020). Radford, A. , Kim, J. W. , Hallacy, C. , Ramesh, A. , Goh, G. , Agarwal, S. , Sastry, G. , Askell, A. , Mishkin, P. , Clark, J. , Krueger, G. & Sutskever, I. Learning transferable visual models from natural language supervision. arXiv:2103. 00020 [cs], (2021). Ramesh, A. , Pavlov, M. , Goh, G. , Gray, S. , Voss, C. , Radford, A. , Chen, M. & Sutskever, I. Zero-shot text-to-image generation. arXiv:2102. 12092 [cs], (2021). . , Zadbood, A. , Hasenfratz, L. , Keshavarzian, N. , Chen, J. , Honey, C. J. , Yeshurun, Y. , Regev, M. , Nguyen, M. , Chang, C. H. C. , Baldassano, C. , Lositsky, O. , Simony, E. ,. , Choe, G. , Goldstein, A. , Vanderwal, T. , Halchenko, Y. O. , Norman, K. A. & Hasson, U. Narratives: fMRI data for evaluating models of naturalistic language comprehension. preprint, Neuroscience (2020). Jawahar, G. , Sagot, B. & Seddah, D. learn about the structure of language? of the 57th of the Association for Computational Linguistics, 3651–3657, Florence, Italy, (2019). Association for Computational Linguistics. T. , Debut, L. , Sanh, V. , Chaumond, J. , Delangue, C. , Moi, A. , Cistac, P. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various research papers related to language models and fMRI data for natural language comprehension without mentioning advanced algorithms predicting emotions or any relationship with brain scans.


Input: Task: corroborate the claim using the given text. Claim: AI algorithms are used in diagnostics to analyze medical imaging more quickly. [SEP] Text: 11Receiver operating characteristic (ROC) curveFull size imageComparison with existing approachesThe EfficientNet-B7 model for breast ultrasound image classification shows several clear advantages over older methods. It delivers higher accuracy and precision compared to traditional machine learning models and earlier deep learning approaches, which often struggle with false positives and false negatives. The model achieves better ROC-AUC scores, reflecting its superior ability to distinguish between different classes, whereas older models typically score lower due to less advanced feature extraction. Its error metrics, including low Error (MAE), Error (MSE), and (RMSE), indicate that it makes more accurate predictions with fewer mistakes. Additionally, EfficientNet-B7 uses Grad-CAM to provide visual explanations of its decisions, improving transparency and trust in clinical settings. Table 7; Fig. 12 show that the EfficientNet-B7 model outperforms other methods, achieving an accuracy of 99. 18% and a precision of 96. 21%. Table 7 Performance comparison with existing modelsFull size table Fig. 12Comparative analysis of accuracy and precision across breast ultrasound classification studiesFull size imageThis study shows, EfficientNet-B7 model demonstrates superior performance in breast ultrasound image classification, achieving the highest accuracy of 99. 14% compared to various traditional and deep learning models. DiscussionThe utilization of EfficientNet-B7, renowned for its scalable architecture, ensures that our model can handle high-resolution medical images effectively, crucial for detailed feature extraction in breast ultrasound images. By incorporating comprehensive data augmentation techniques like RandomHorizontalFlip, RandomRotation, and ColorJitter, we enhance the model’s robustness against variations in image presentation, which is pivotal for generalizing across different clinical environments. Moreover, the application of thorough preprocessing and normalization ensures consistent input quality, facilitating stable model training and convergence. Our strategy of fine-tuning the entire network, coupled with early stopping, allows for precise model adjustment to the dataset while preventing overfitting, ensuring the model remains applicable and reliable for further analytical pursuits. This robust analytical framework is supported by detailed performance evaluations using metrics such as precision, recall, F1-scores, and confusion matrices, providing a deep insight into the model’s diagnostic capabilities and ensuring its efficacy and reliability for extended clinical and research applications. [SEP] Justify:

Output: The text corroborates the claim by describing the EfficientNet-B7 model, which utilizes deep learning for breast ultrasound image classification. It details how this model enhances diagnostic accuracy and speed in medical imaging, indicating its role as an AI algorithm specifically designed for analyzing images more effectively and efficiently compared to older methods.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: The concept of green tourism aligns closely with the principles of sustainable development and ecotourism. It emphasizes the responsible utilization of tourism resources, coupled with a conscientious regard for society and the environment. Simultaneously, it aims to provide tourists with comfortable, secure, and health-enhancing tourism products6. As ecological consciousness continues to gain traction and tourists increasingly appreciate and respect multiculturalism and natural heritage, green and low-carbon tourism products are poised to take center stage in the future of the tourism industry7. These products not only cater to the evolving needs of travelers but also align with the broader shift towards sustainable and environmentally conscious practices. Virtual tourism is a novel business concept born from the fusion of virtual reality and the realm of travel and tourism8–9. Leveraging computer simulation technology, virtual reality, and augmented reality advancements10, it empowers tourists to embark on immersive journeys from the comfort of their homes11. This innovative form of tourism has been heralded by some scholars as a potential substitute for physical travel, offering convenient avenues for exploring protected historical sites and accessing remote natural wonders12. Virtual tourism can minimize excessive exploitation and damage to natural resources such as water resources, land, and wildlife habitats to the greatest extent. It does not require significant transportation and energy consumption, reducing carbon footprint and greenhouse gas emissions. The confluence of technological innovation and the disruptive force of the COVID-19 pandemic has acted as a catalyst and an accelerator for the green tourism industry, propelling supply-side reforms and propelling it toward high-quality development. Green tourism places a premium on the principles of physical and mental well-being, environmental stewardship, low-carbon practices, and sustainable development—values that resonate with the ethos promoted by virtual tourism13. In this context, virtual tourism has the potential to drive innovative, forward-thinking, and pragmatic transformations within the green tourism sector, enabling maximum conservation of the natural and cultural environment of tourist destinations during the virtual experience14. It stands as a bridge between the realms of real and virtual, fostering sustainable tourism practices and ecological preservation. 2. 2 Virtual tourism experiences In the realm of tourism research, external factors that influence tourists' behavioral intentions, such as individual characteristics, situational factors, and physical and social interactions, are widely recognized as pivotal elements shaping the tourist experience15–16. [SEP] Justify:

Output: The text contrasts the claim by elaborating on various aspects of green tourism and virtual tourism but does not guarantee a positive experience for all. It discusses the principles of responsible tourism and acknowledges the complexities of tourism experiences influenced by external factors like individual characteristics and social interactions. This suggests that while eco-tourism may align with sustainable practices, it does not ensure a universally positive experience for all involved.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Login to your account Forgot password? password must have 1 characters or more and contain 3 of the following: a lower case character, an upper case character, a special character or a digit password has been changed Create a new account user Can't sign in? Forgot your password? Enter your email address below and we will send you the reset instructions If the address matches an existing account you will receive an email with instructions to reset your password. Username Can't sign in? Forgot your username? Enter your email address below and we will send you your username If the address matches an existing account you will receive an email with instructions to retrieve your username "skipMainNavigation"close: Artificial IntelligenceRadiology: Cardiothoracic ImagingRadiology: your & in anywhereSearchSearchSearch 0 Sign in 0628. Address correspondence to Y. L. (email: [email protected]). Chen*Gunvant Chaudhari*Christopher P. HessOrit A. GlennLeo P. SugrueAndreas M. Rauschecker**Yi Li** Published Online:Jul 19 2022 favoritesCiteTrack 2019. The GCA at MRI was manually calculated. After exclusion criteria were applied, T1- and T2-weighted MRI scans were preprocessed with skull stripping, linear registration, z scoring for normalization, and downsampling. A three-dimensional regression CNN was trained to predict GCA using mean absolute error (MAE) as its loss function. Attention maps were calculated using layer-wise relevance propagation. Models were validated on an external test set from the of Health (NIH). Model MAEs were compared using Kruskal-Wallis and Mann-Whitney tests. total of 518 neonates and infants (mean GCA, 67 weeks ± 33 [SD], 56% male) was included, comprising 469 T1-, 438 T2-, and 389 T1- and T2-weighted studies. Across 10 runs, MAEs of T1-, T2-, and T1- and T2-weighted networks were 9. 8 ± 2. 3, 9. 1 ± 1. 9, and 7. 7 ± 1. 7 weeks, respectively. [SEP] Justify:

Output: The text provides information about an MRI study involving various preprocessing and analysis methods for brain scans, but it does not mention anything about predicting emotions or advanced algorithms related to emotions based on brain scans. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI algorithms are used in diagnostics to analyze medical imaging more quickly. [SEP] Text: The integration of EfficientNet-B7 in our diagnostic framework optimizes the processing of high-resolution breast ultrasound images, capturing crucial pathological features accurately. Extensive data augmentation and rigorous preprocessing protocols enhance the model’s robustness and consistency across varied clinical imaging conditions. By fine-tuning the entire network and employing early stopping mechanisms, our approach ensures deep learning tailored to the specific nuances of ultrasound imagery and maintains model generalizability. These methodological choices bolster the model’s role not only as a diagnostic tool but also as a platform for ongoing clinical research, confirming its potential to improve patient outcomes by enabling early and precise detection of breast pathologies. Incorporating the EfficientNet-B7 model combined with sophisticated image processing techniques offers substantial advantages for real-time medical systems, particularly in diagnostic applications. The architecture of EfficientNet-B7 balances accuracy and computational efficiency, essential for the swift and reliable diagnosis required in medical emergencies. This model’s robustness against diverse image presentations and lighting conditions, achieved through advanced data augmentation techniques like RandomHorizontalFlip, RandomRotation, and ColorJitter, ensures its effectiveness across varying clinical settings. Additionally, the integration of Explainable AI (XAI) methods such as Grad-CAM enhances transparency, allowing clinicians to understand and trust AI-assisted diagnostic decisions. These features make the classification method highly suitable for real-time applications in medical settings, supporting rapid clinical decision-making and potentially improving patient outcomes by reducing the time to diagnosis and treatment initiation. The EfficientNet-B7 model and its techniques demand significant computational resources, which might not be accessible in all settings. Its performance depends on the quality and diversity of the training data, with any biases potentially impacting results. Although Grad-CAM offers visual explanations, they may be difficult for clinicians to interpret. Integrating this complex system into clinical workflows necessitates careful planning and validation. Additionally, the model is tailored for breast ultrasound images and may require adjustments for use with other types of medical imaging. ConclusionThis study presents a robust approach to classifying breast ultrasound images into benign, malignant, and normal categories, utilizing the advanced EfficientNet-B7 architecture. By leveraging sophisticated data augmentation techniques, including RandomHorizontalFlip, RandomRotation, and ColorJitter, this methodology effectively addresses the challenges of class imbalance and enhances the model’s robustness. [SEP] Justify:

Output: The text corroborates the claim by detailing the role of the EfficientNet-B7 model in optimizing the processing of breast ultrasound images, which enhances diagnostic accuracy and speed. Specifically, it highlights that 'the architecture of EfficientNet-B7 balances accuracy and computational efficiency, essential for the swift and reliable diagnosis required in medical emergencies.' This indicates the use of AI algorithms to analyze medical imaging more quickly.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: In the era of the experience economy, as tourists' travel experiences grow increasingly diverse, tourism products evolve and upgrade, and the concept of tourism consumption matures, tourists' demands for their travel experiences are also evolving17. No longer content with traditional sightseeing and everyday travel offerings, tourists now seek a more enriching and spiritually fulfilling green tourism experience18. The green tourism experience places a significant emphasis on the preservation and enhancement of the environment, society, and culture, all while prioritizing the quality of the overall travel experience. Central to the concept of virtual tourism is the virtual tourism experience, wherein tourists engage in tourism activities with the aim of acquiring unique and distinct experiences19. The virtual tourism experience involves a conscious integration of thoughts and emotions within virtual tourism scenarios, representing a multifaceted process of individual psychological, social, and cognitive interactions among tourists20. Green and sustainable travel, apart from safeguarding the natural and human environment, also offers travelers distinctive and enriching travel experiences. 3 Research objects and methods 3. 1 This study employed a mixed-method approach within an exploratory sequential design framework. The research began with qualitative methods to explore the factors influencing Chinese green tourists' behavioral intentions toward virtual tourism experiences and to uncover the underlying mechanisms. Following this, quantitative methods were used to validate the developed mechanism model and assess its broader applicability. All research protocols, including interviews and survey distribution, were thoroughly reviewed and approved by the of Shandong University. The study was conducted in strict accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Informed consent was obtained from all participants before their involvement in the study. Informed consent was obtained from all the subjects and/or their legal guardians involved in the study. The confidentiality of all participants was rigorously maintained. Personal identifiers were anonymized, and the data was securely stored in encrypted files accessible only to the research team. All collected data was used solely for academic purposes. 3. 2 Data collection and data testing In the first phase, Study 1 embraced a qualitative research approach. It involved in-depth interviews with 36 Chinese green tourists to gain insights into their perspectives on virtual travel experiences. Utilizing face-to-face and semi-structured in-depth interviews, a comprehensive mechanism model elucidating the factors influencing Chinese green tourists' behavioral intentions towards virtual tourism experiences was meticulously crafted. In the second phase, Study 2 employed a quantitative research methodology through a questionnaire survey. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while green and sustainable travel can provide distinctive and enriching experiences, it does not guarantee a universally positive experience for all travelers and communities involved. The focus on evolving tourist demands and the complexity of individual psychological, social, and cognitive interactions suggest that experiences can vary greatly among tourists.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Attention map analysis demonstrated increased network attention to the cerebellum, posterior white matter, and basal ganglia signal in neonates with GCA of less than 40 weeks and the anterior white matter signal in infants with GCA of more than 120 weeks, corresponding to the known progression of myelination. The T1- and T2-weighted network tested on the external NIH test set had an MAE of 9. 1 weeks, which was reduced to 5. 9 weeks with further training using half the external test set (P <. 001). three-dimensional convolutional neural network can predict the gestationally corrected age of neonates and infants aged 0–25 months based on brain myelination patterns on T1- and T2-weighted MRI scans. © RSNA, 2022 Online supplemental material is available for this 7. 7 weeks. ■ The network spatial patterns of attention corresponded with known age-dependent patterns of myelination. ■ When tested on an external test set of normal MRI scans, network MAE was 9. 1 weeks and was further reduced to 5. 9 weeks after additional training on half of the external data set (P <. 001). plays a critical role in facilitating signal conduction in the brain and is essential for basic neurologic functions, including learning and information processing (1–3). Myelination during infancy follows a predictable spatial pattern during the first 2 years of life, and these changes can be depicted by MRI. With myelination, T1-weighted images demonstrate increasing areas of T1 hyperintensity, reflecting lipid content in the myelin sheaths, and T2-weighted images demonstrate increasing areas of T2 hypointensity, reflecting decreased axonal water content (1,4–6). T1 and T2 correlates of myelination are indicators of brain maturation, with delayed myelination on images corresponding to clinical developmental delay (7). In clinical practice, radiologists assess the appropriateness of myelination by assessing specific myelination milestones, which should occur at predictable intervals based on gestationally corrected age (GCA) (4). Examples of such milestones for T1-weighted imaging include myelination of the posterior limb of the internal capsule by 40 weeks GCA and myelination of the anterior limb of the internal capsule by 2–3 months (Fig 1) (4). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the use of three-dimensional convolutional neural networks to predict gestational age in neonates based on brain myelination patterns, rather than predicting emotions based on brain scans. There is no mention of emotion prediction or advanced algorithms in relation to that context.


Input: Task: corroborate the claim using the given text. Claim: AI algorithms are used in diagnostics to analyze medical imaging more quickly. [SEP] Text: Fine-tuning EfficientNet-B7 on the BUSI dataset, combined with an early stopping mechanism, resulted in a remarkable classification accuracy of 99. 14%. This approach not only improves the model’s performance but also integrates Explainable AI (XAI) technique like Grad-CAM, which enhance the interpretability of the model’s predictions. These XAI methods provide critical insights into the specific regions and features of ultrasound images that influence the classification outcomes, thereby increasing the model’s transparency and trustworthiness in clinical settings. The findings of this study indicate that integrating scalable CNN architectures like EfficientNet-B7 with advanced augmentation and XAI techniques can substantially improve automated diagnostic systems, supporting early detection and treatment of breast cancer. Future work could expand this methodology to larger and more diverse datasets to further validate the model’s generalizability across different imaging conditions and patient demographics. Additionally, exploring the integration of other state-of-the-art architectures and ensemble learning techniques could enhance classification performance. Expanding this approach to other medical imaging modalities and incorporating real-time processing capabilities would be valuable extensions. Furthermore, developing a comprehensive framework for integrating these advanced models into clinical workflows, including real-time deployment and user-friendly interfaces for clinicians, would be critical to facilitate broader clinical adoption. The proposed EfficientNet-B7 based framework demonstrates significant advancements in breast ultrasound image classification, achieving exceptional accuracy and providing interpretable insights through XAI techniques. This work underscores the potential of leveraging advanced deep learning architectures and targeted data augmentation strategies to enhance the diagnostic accuracy and reliability of automated systems in medical imaging. By improving the classification of breast ultrasound images, this study contributes significantly to the early detection and diagnosis of breast cancer, offering a promising tool to support clinicians in making informed and accurate decisions. Ultimately, this approach aims to improve patient outcomes and treatment efficacy, highlighting the crucial role of advanced AI technologies in advancing healthcare diagnostics. Availability of data and materials The data that support the findings of this study are openly available at 85. 37. 21. 92. 113. 16. 3047. 5377. 95. 2242. 27. 2021. p. 437–42 IEEE. 2022. p. 39–48. 4292. 52. 2021. p. 241–52. 991. 9. 3244. 2024. Hadi TH. [SEP] Justify:

Output: The text corroborates the claim by discussing how advanced AI technologies, particularly the EfficientNet-B7 deep learning architecture, are utilized to improve the accuracy and performance of automated diagnostic systems in medical imaging, specifically for breast ultrasound. It emphasizes that these technologies not only enhance classification accuracy but also integrate processes that support clinical decision-making, aligning with the claim that AI is used in diagnostics to analyze medical imaging more efficiently.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: Virtual tourism, on the other hand, represents a form of tourism that boasts low resource consumption, minimal environmental pollution, heightened product value, and efficient production methods. It has garnered significant favor among a subset of environmentally conscious tourists26. In interviews conducted for this study, it became evident that environmental preservation and sustainability serve as primary motivators for green tourists when selecting virtual tourism as their preferred choice. These factors play a pivotal role in igniting their enthusiasm for the virtual tourism experience. Furthermore, it was observed that the more comprehensive green tourists' understanding is regarding the environmental conservation and sustainability aspects of virtual tourism, the more robust their experiential attitudes and behavioral intentions become. (2) Tourism value orientation The value inherent in tourism is contingent upon tourists' comprehension of its essence, encompassing their value orientation and the criteria by which they judge the value of their beliefs and spiritual experiences related to tourism27. At the heart of green tourism lies the commitment to provide tourists with a form of intellectual nourishment. This commitment is built on the foundational principles of environmental conservation and ecological equilibrium, ensuring that green tourists find a profound sense of spiritual belonging. The positive cognitive orientation of green tourists towards virtual tourism serves to foster their awareness and interest in the virtual tourism experience, ultimately influencing their experiential attitudes and behavioral intentions when engaging in virtual tourism activities. (3) Virtual tourism characteristics Virtual tourism offers unique attributes such as sustainability, environmental consciousness, and the ability to transcend the constraints of time and space, which allow green tourists to fulfill their wanderlust without imposing unnecessary burdens on the environment and society. The evolution of virtual tourism transforms green tourists' tourism pursuits into simulations of social interactions and travel experiences, effectively addressing a spectrum of psychological needs for green tourists28. The environmentally friendly and sustainable aspects of virtual tourism facilitate the swift acceptance of this mode by green tourists. This, in turn, has a favorable impact on their experiential attitudes, ultimately culminating in active engagement towards virtual tourism experiences. Therefore, the following hypotheses are proposed: Hla: Green tourism cognition has a significantly positive impact on Chinese green tourists' attitudes towards virtual tourism experiences. Hlb: Tourism value orientation has a significantly positive impact on Chinese green tourists' attitudes towards virtual tourism experiences. Hlc: Virtual tourism characteristics have a significantly positive impact on Chinese green tourists' attitudes towards virtual tourism experiences. H2a: Green tourism cognition has a significantly positive impact on Chinese green tourists' behavioral intentions towards virtual tourism experiences. [SEP] Justify:

Output: The text contrasts the claim by discussing virtual tourism as an alternative to eco-tourism, emphasizing that it caters to environmentally conscious tourists and promotes sustainability without the need for physical travel. It implies that while virtual tourism offers positive experiences for those focused on environmental conservation, it may not guarantee a universally positive experience for all travelers and communities involved as the claim suggests.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Discordance between a neonate’s or infant’s GCA and expected myelination milestones suggests abnormal myelination. However, determining appropriateness of myelination can be subjective and challenging in clinical practice (8,9), especially for those without dedicated pediatric neuroradiology expertise, with error rates for determining delayed myelination near 15% (10). Unfamiliarity with normal myelination milestones is among the leading reasons for incorrect diagnosis of delayed myelination, with consequences including unnecessary or delayed diagnostic work-up for genetic or metabolic syndromes (10,11). Figure 1: Expected neonate and infant brain myelination milestones on T1- and T2-weighted images. T1-weighted (green) and T2-weighted (purple) imaging milestones are shown. The span of each milestone represents the range of ages when the achievement of the myelination milestone is considered appropriate. Adapted from Barkovich and Raybaud (4). Figure 1:Download as Powerintelligence may assist with the diagnosis of delayed myelination and has shown great potential in augmenting medical image analysis (12,13). Machine learning using convolutional neural networks (CNNs) has thus far been successfully applied to estimate brain age from neuroimaging across a wide age range from neonates to adults (14–23). However, there is a lack of studies in the literature that have trained and validated a CNN to determine neonate and infant brain age by specifically detecting brain myelination patterns. This study aims to develop a CNN model capable of estimating neonatal and infant GCA from 0 to 25 months of age, based on brain myelination on MRI scans. Materials and MethodsThis was a Portability and Accountability–compliant institutional review board–approved retrospective study, with waiver of informed consent. The study population of interest included neonates or infants aged 0–25 months, with normal myelination on brain MRI scans identified on the radiology report. The radiology database from a single tertiary care academic medical center was queried consecutively for clinical reports for MRI scans showing normal brain myelination between January 1995 and June 2019, yielding 5838 MRI studies. [SEP] Justify:

Output: The text discusses topics related to brain imaging, myelination milestones, and machine learning in medical imaging. However, it does not address any claims regarding advanced algorithms predicting emotions based on brain scans. Therefore, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: In addition to AI and ML, we see the rise of that has potential to act like, look like, talk like and reason like human, and eventually becoming an essential part of an organizational business infrastructure worldwide [1]. Some experts compare level of intelligence of new called Sophia to that of a one month old new born baby that is at the very beginning of discovering the world around and is beginning to observe and to learn to communicate with his or her parents. Yet, very young and tiny, babies have a that may be compared to a computational capacity of most performant Supercomputer in the world today. Many experts suggest that there are fundamental differences between the AI and (NI) and that future evolution of AI driven may have strong impact on the future Industry 5. 0 and 6. 0, Business to Business (B2B) and (mC). Given recent COVID relates crisis, applications of AI in business, industry, government, academia and other sectors have become ubiquitous showing more and more examples of Agents application. The next section, presents examples of the AI driven applications including chatbots and ChatGPT, and discus what, how and where they are. Advertisement 2. What are the Chatbots? Chatbots are one of the practical examples of AAI driven cyber automation. The software capable to facilitate human-machine conversation is known today as a chatbot. A chatbot software simulates and drives a real-time interaction between a customer a human partner, while bridging a client system and information acquisition domains. Chatbot is a conversational agent that utilizes AI to interpret the text of the chat using Processing (NLP). Instead of direct communication with human service personnel, the customer/client can make conversation via text or voice with the chatbot software [2]. The chatbots utilize the AI embedded conversational systems capable to recognize customers and information systems database driven keywords or word patterns to provide the human-like answer in real-time. Chatbots have become an essential part of organizational business infrastructures while improving a customer service with faster and cost effective best quality support. 2. 1 Chatbots are not perfect yetThe rise of AAI and chatbots have created collection of new tools that are often applied by students, faculty and others to write scholastic essays, 2. [SEP] Justify:

Output: The text corroborates the claim by explaining how chatbots serve as practical examples of AI-driven applications. It states that chatbots facilitate human-machine conversation and enhance customer service by providing faster and cost-effective support. While the context primarily discusses the business applications of chatbots, the mention of chatbots as 'conversational agents' equipped with AI capabilities implies their potential use in healthcare settings for preliminary diagnosis and patient engagement.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: H3c: Perceived risks have a significantly negative impact on Chinese green tourists' attitudes towards virtual tourism experiences. H4b: Perceived benefits have a significantly positive impact on Chinese green tourists' behavioral intentions towards virtual tourism experiences. H4a: Perceived immersion has a significantly positive impact on Chinese green tourists' behavioral intentions towards virtual tourism experiences. H4c: Perceived risks have a significantly negative impact on Chinese green tourists' behavioral intentions towards virtual tourism experiences. 4. 5. 3 Experience attitude In 1967, Fishbein introduced a theoretical model of rational behavior, laying the foundation for the widespread recognition of the attitudinal-behavioral intention model among scholars32. The rational behavior theory model emphasizes that an individual's attitudes and subjective norms significantly influence their behavioral intentions, which, in turn, accurately predicts subsequent individual behavior. In 1985, Ajzen further expanded on this concept with the theory of planned behavior, reinforcing the pivotal role of individual attitudes in shaping behavioral intentions. Green tourists develop either positive or negative emotional attitudes towards virtual tourism based on their analysis of the amalgamated internal and external information available. These attitudes, in turn, profoundly affect their intentions regarding virtual tourism experiences. When green tourists hold a favorable view of virtual tourism, their inclination to partake in virtual tourism experiences increases. Once this inclination reaches a certain threshold, it translates into actual experiential behavior. Therefore, the following hypothesis is proposed: H5: Chinese green tourists' attitudes towards virtual tourism experiences has a significantly positive impact on their behavioral intentions. 4. 5. 4 Experience type factors Previous studies have found that each tourist has his or her own ideas and motivations for tourism experience, and different types of tourism experience can produce different degrees of behavioral intentions for tourists. Furthermore, distinct types of tourism experiences can generate varying levels of attitudes and behavioral intentions among tourists. Therefore, the selection of a tourism experience is invariably influenced by both internal psychological factors and external environmental factors. Drawing upon grounded theory and an extensive analysis of prior research literature, virtual tourism experiences primarily encompass two main categories: natural scenery experiences and cultural landscape experiences. Various experience types often mirror distinctions among consumer demographics, values, consumption p4. 5. 5 Social situational factors Social situational factors denote external variables beyond tourists' control in the realm of tourism experience behavior, which predominantly signify outcomes arising from the interplay of diverse economic, cultural, and social factors that develop around green tourists' tourism activities. [SEP] Justify:

Output: The text contrasts the claim by indicating that perceived risks can have a significantly negative impact on tourists' attitudes and behavioral intentions regarding virtual tourism experiences. Specifically, it states that 'perceived risks have a significantly negative impact on Chinese green tourists' attitudes towards virtual tourism experiences,' suggesting that not all travelers will have a positive experience, as eco-tourism can involve various perceived risks affecting different individuals.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Exclusion criteria included random reduction of the 0–3-month age bin to balance age distribution (24); studies with myelination at the lower limits of normal; studies missing gestational age at birth in medical records; MR spectroscopy spine, face, orbits, and neck studies for which the field of view did not include complete brain imaging; studies with reported global white matter or structural abnormality; and contrast-enhanced, truncated, or mislabeled studies (Fig 2, 15. 4 years (95% CI: 14. 6, 16. 3) of post-fellowship experience at the time of interpretation. Figure 2: Flowchart shows MRI study selection for T1-, T2-, and both T1- and T2-weighted convolutional neural networks. Figure 2:Download as Powerand infant characteristics for T1-, T2-, and both T1- and T2-weighted MRI studies are provided in Table 1. Races and ethnicities were self-reported and were gathered from the medical record to demonstrate diversity of the training set. To account for prematurity, GCAs at the time of MRI were manually calculated by summing the gestational age at birth and the chronologic ages at the time of imaging, and GCAs were used to train the algorithm. The study indications were varied, with the top five reported not mutually exclusive indications being preterm status (196 of 518 patients [38%]); seizure or epilepsy (65 of 518 patients [13%]); developmental delay (58 of 518 patients [11%]); suspected hypoxic-ischemic injury (36 of 518 patients [7%]); and suspected hemorrhage (29 of 518 patients [6%]). Table 1: Neonate and of and manufacturers included (99. 0% of studies) and Systems (1. 0%). Of the final sequences, 57. 6% were three-dimensional (3D), and 42. 4% were two-dimensional (2D) (Table 2). Table 2: MRI Parameters for the in Comparison with an for ValidationModel for 3D CNN was used to predict brain age from neonate and infant MRI scans. Prior to network input, data were divided into training, validation, and test sets by a 70%:20%:10% ratio. The neural network architecture was based on a previous 3D CNN for adult brain age prediction (18). [SEP] Justify:

Output: The text does not provide any information related to the claim about advanced algorithms predicting emotions based on brain scans. Instead, it details criteria for study selection and descriptions of MRI studies and neural network architectures used for predicting brain age. Therefore, this text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 2 New generation of ChatbotsEvolution of AI driven chatbots brough a new Pre-(ChatGPT) technology utilizing a dialogue-based AI chatbot capable of understanding natural human language and generating quite detailed human-like written text. These ChatGPTs are part of text-generating AIs technologies and are becoming integrated in business, industry, government and other sectors worldwide. GTP systems are trained by AI & ML, and are capable to answer questions via a conversational interface. These new OpenAI systems are trained on a very large sample of text adopted directly from the Internet information System, enabling a dialogue format for ChatGPT to answer or follow-up questions, to admit its mistakes, to challenge incorrect premises, as well as to reject inappropriate requests [3]. The results produced by ChatGPT are based on the data available in the organizational information systems database only. The ChatGPT today, are not able to collect data from primary sources independently and to advance the AI driven technological evolution in social sciences. There are important challenges to make ensure data integrity and data accuracy in conjunction with the structural and legal frameworks regarding the copyright and the author’s rights. There are critical challenges to make sure that the future impact of AI driven chatbots on mental health is minimum. Current studies show, that the anxiety and paranoia levels have increased when interacting with chatbots acting like humans instead of humans communications with humans. Some social applications may mislead people with chatbots acting like humans to help people to socialize utilizing chatbots acting like humans. This may contribute to paranoia, and some users may not be able to distinguish whether they are talking to humans or chatbots. The studies show that extensive use of ChatGPT may contribute to paranoia caused by frequent interactions with chatbots instead of interaction with humans [4]. Advertisement 3. applications in B2BToday, the chatbots are well accepted in the B2B providing a business value and promote framework for training chatbots in order to to better serve B2B customers. Chatbots have potential to provide support for fast and effective B2B shopping experience by providing personalized support and guidance, helping customers to find the right products or services quickly and efficiently. The B2B chatbots are trained to understand the unique client’s needs and p4. [SEP] Justify:

Output: The text corroborates the claim by discussing how new AI-driven chatbots, such as those powered by ChatGPT, operate using a dialogue-based format that understands natural human language and generates human-like text. It mentions that these chatbots are well accepted in various sectors, including healthcare, implying their potential role in patient engagement. The text references their ability to answer questions in a conversational manner, which is critical in preliminary diagnosis and patient engagement scenarios.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: Based on the findings derived from grounded theory, this study identifies the social situational factors within virtual tourism, which encompass Industry policies and social standards. (1) Industry policies Industry policies encompasses various external mechanisms such as state policy guidance, economic incentives, and industry norms. The growth of the virtual tourism industry forms the foundation for tourists' virtual tourism experiences and influences their behavior within this context33. Currently, the progress of the virtual tourism industry is still constrained by factors like industry quality standards, policy directives, and mechanisms governing entry into the tourism market, all of which impact the industry's ability to advance swiftly. In China, the virtual tourism industry has experienced rapid growth, with increased oversight and enhancements to the quality certification and standardization system by relevant authorities. These existing factors contribute to enhancing green tourists' behavioral intentions towards virtual tourism experiences. (2) Social standards Social standards represent social psychological factors, wherein individual members are guided or constrained by behavioral rules and standards to ensure the achievement of group objectives and the consistency of group activities. Individuals are not entirely autonomous within society; instead, they are inherently social beings, committed to fulfilling social roles and seeking recognition from others. Tourists often hold misconceptions about virtual tourism, while their immediate social circles may be skeptical or resistant to it. To garner recognition and appreciation, and to avoid potential ridicule or mockery from their social groups, green tourists make every effort to conform to the standards of social subjective norms. This orientation toward social acceptance is a key factor influencing green tourists' acceptance of virtual tourism experiences. Therefore, the following research hypotheses are proposed: H7a: The development of virtual tourism industry has a significantly positive impact on Chinese green tourists' behavioral intentions. H7b: The social standards of virtual tourism has a significantly positive impact on Chinese green tourists' behavioral intentions. 4. 6 Factors model of Chinese green tourists' behavioral intentions towards virtual tourism experiences Based on grounded theoretical analysis and the research hypotheses outlined above, this study formulates the factors model of Chinese green tourists' behavioral intentions towards virtual tourism experiences. The model as shown in Fig. 1. 5 Sub-study 2: Empirical test and analysis of Chinese green tourists' behavioral intentions towards virtual tourism experiences 5. 1 5. 1. 1 on the fundamental framework for assessing Chinese green tourists' intentions to engage in virtual tourism experiences, this study conducted a comprehensive review of existing literature on virtual tourism experiences and gathered qualitative insights from interviewees. [SEP] Justify:

Output: The text contrasts the claim by discussing the challenges and limitations within the virtual tourism industry, indicating that the experience is not guaranteed to be positive. It outlines how social standards and industry policies influence tourists' behaviors and perceptions, which suggests that not all travelers will have a positive experience due to existing misconceptions and social pressures.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: It consisted of five blocks of 3 × 3 × 3 3D convolution, batch normalization, rectified linear unit (ReLU) activation, and 2 × 2 × 2 maximum pooling layers, followed by a block of 1 × 1 × 1 3D convolution, batch normalization, ReLU activation, 2 × 2 × 2 average pooling layers, and finally a dropout layer before the linear output layer (Fig 3). The filters used for each 3D convolution layer were 16, 64, 128, 256, 256, and 64, respectively. All layers were randomly initialized with the He et al (25) normal method. The network training was repeated for 10 runs, with data splits created such that each sample was part of the test set exactly once in the 10 runs (10-fold cross-validation). Each network was trained for 50 epochs using the Adam optimizer with a batch size of 16 and an adaptive learning rate starting at 0. 001. Further details are provided in 1. 0. 9; to generate attention maps (26). Average attention maps were calculated for each age range by averaging the maps of images within those age ranges across all 10 runs, so each image in the data set was represented exactly once. To create maps of two separate age ranges, maps were separately scaled to between 0 and 1, and overlapping pixels with intensity greater than 0. 2 were discarded to highlight the difference between the maps. For the combined map, all pixels with values above 0. 8 were set to one color for each age range. An external test set consisting of normal MRI scans of children born at term (between 37 weeks, 4 days to 42 weeks, 3 days of gestation) from the publicly available of Health (NIH) MRI database was extracted for further evaluation of our model. Data were accessed from the of portal with appropriate permissions (27). Scans from children younger than 760 days (25 months) were extracted (n = 226) and run through the same preprocessing steps as our institutional data: skull stripping, registration to a common template, normalizing signal intensity via z scoring, and downsampling by 2 × 2 × 2. The internally trained model was tested on this external data set. Subsequently, the best-performing internally trained model was further trained on one-half of the external NIH data for the purpose of fine-tuning the model to the specific acquisition parameters used in the external data set. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the technical details of a convolutional neural network architecture and its training process without discussing any connection between advanced algorithms, brain scans, or the prediction of emotions.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: Home E-submission us and scope About the journal Editorial board Copyright, open access, and archiving policy Contact us 222. Published online: April 10, 2023 DOI: 인공지능 챗봇의 질병 진단 및 환자 치료에 대한 답변은 신뢰할 수 있는가? 허선 한림대학교 의과대학 기생충학교실 및 의학교육연구소 Can we trust AI chatbots’ answers about disease diagnosis and patient care? Sun Huh, MD Department of Parasitology and Institute of Medical Education, College of author: E-mail: shuh@hallym. ac. kr 20, 2023 12, 2023© is an 4. 0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Several chatbots that utilize large language models now exist. As a particularly well-known example, ChatGPT employs an autoregressive modeling process to generate responses, predicting the next word based on previously derived words. Consequently, instead of deducing a correct answer, it arranges the most frequently appearing words in the learned data in order. Optimized for interactivity and content generation, it presents a smooth and plausible context, regardless of whether the content it presents is true. This report aimed to examine the reliability of ChatGPT, an artificial intelligence (AI) chatbot, in diagnosing diseases and treating patients, how to interpret its responses, and directions for future development. Current Concepts: Ten published case reports from Korea were analyzed to evaluate the efficacy of ChatGPT, which was asked to describe the correct diagnosis and treatment. ChatGPT answered 4 cases correctly after being provided with the patient’s symptoms, findings, and medical history. The accuracy rate increased to 7 out of 10 after adding laboratory, pathological, and radiological results. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that AI chatbots, specifically ChatGPT, can assist in disease diagnosis and treatment. It indicates that ChatGPT was able to give correct answers in 4 out of 10 cases after being presented with patient symptoms and findings. The accuracy improved to 7 out of 10 when additional medical information was provided, highlighting the chatbot's ability to aid in preliminary diagnosis.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: 467, P<0. 001), tourism value orientation cognition (β = 0. 356, P<0. 001), and virtual tourism characteristics (β = 0. 262, P<0. 001), positively impact green tourists' experience attitudes towards virtual tourism experiences. Firstly, virtual tourism cognition, by offering positive information and allure about virtual tourism, enhances the positive attitudes. Secondly, tourism value orientation cognition prompts individuals to place greater emphasis on the practical value and benefits of virtual tourism, thereby fostering a positive experiential attitudes. Lastly, virtual tourism characteristics enables green tourists to better understand and appreciate the unique features of virtual tourism, promoting the formation of a positive experiential attitudes. These cognitive factors interact with each other, collectively providing support for the formation of a positive virtual tourism experiential attitudes among green tourists. As a result, hypotheses H1a, H1b, and H1c were supported. 5. 4. 2 The relationship between perception factors and green tourists' experience attitudes Perceived benefits (β = 0. 164, P<0. 001) and perceived immersion (β = 0. 521, P<0. 01) within the perceptual factors have a positive impact on green tourists' experience attitudes towards virtual tourism experiences, while perceived risks (β=-0. 156, P<0. 001) exert a negative influence. Firstly, when individuals recognize significant benefits and positive outcomes linked to virtual tourism, such as increased knowledge, enjoyment, or relaxation, it inherently cultivates a positive attitudes. Secondly, the immersion in a virtual tourism experience, marked by a profound sense of engagement and absorption in the virtual environment, enhances the overall experiential attitudes. Conversely, the existence of perceived risks, such as apprehensions about security, privacy, or the reliability of virtual tourism experiences, induces caution and unease among individuals. These uncertainties and negative perceptions contribute to a less favorable attitudes toward virtual tourism. As a result, hypotheses H3a, H3b, and H3c were supported. 5. 4. 3 The relationship between green tourists' attitudes and behavioral intentions The positive impact of green tourists' attitudes (β = 0. 771, P<0. 001) on behavioral intentions towards virtual tourism experiences suggests that when environmentally conscious tourists hold favorable opinions, beliefs, or feelings regarding virtual tourism, it significantly influences their expressed intentions to engage in such experiences. [SEP] Justify:

Output: The text contrasts the claim by highlighting that factors such as perceived risks negatively influence green tourists' experience attitudes towards virtual tourism. It illustrates that while there are positive aspects associated with virtual tourism, significant perceived risks can induce caution and unease among individuals, suggesting that a guaranteed positive experience is not assured for all travelers.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Specifically, one-half of the NIH data set was used to fine-tune all layers of the T1- and T2-weighted model trained on our institutional data for 25 epochs with a learning rate of 0. 0001. The resulting model was tested on the other half of the data set. The original model was then also fine-tuned with the other half of the data set and tested on the independent half, and the results were amalgamated. All values reported are means ± 1 SD, unless otherwise specified. The number of neonates or infants born preterm, sex, and ethnicity in the training data were compared between models by using the χ2 test, and distributions of GCAs were compared with the Kruskal-test because of nonparametric data. To compare absolute error distributions across the T1-, T2-, and T1- and T2-weighted models, including for various GCA subsets, the Kruskal-test was used. To compare between two models, the two-sided Mann-test with continuity correction was used. Pearson linear correlation was used to calculate the strength of correlation between predicted and true GCAs. Linear regression t tests were conducted with the alternative hypothesis of slope greater than zero. P <. 05 indicated a significant difference. Statistical analyses were conducted in Python (version 3. 7) using the SciPy package ( of Our initial search yielded 5838 MRI studies. After exclusion (n = 5320), our final study sample consisted of 518 MRI studies with GCAs between 29 and 151 weeks (Fig 2, Fig E1 [online]). We found no evidence of a difference between the mean or median GCA used for the T1-, T2-, and T1- and T2-weighted models (T1, 65. 0 weeks; T2, 64. 4 weeks; T1 and T2, 61. 9 weeks; P =. 48; Table 1). We also found no evidence of a difference among the three models with respect to preterm status (T1, 48%; T2, 48%; T1 and T2, 49%; P =. 97), sex (T1, 56% male; T2, 57% male; T1 and T2, 58% male; P =. [SEP] Justify:

Output: The text does not address the claim about advanced algorithms predicting emotions based on brain scans. Instead, it focuses on statistical analyses of MRI studies, specifically regarding the comparison of different models without any reference to emotional prediction or algorithms related to it.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그리고, program coding 분야에서도 획기적인 도구로 사용하였다. 의사나 환자, 보호자가 ChatGPT를 활용할 것을 짐작하는 것은 전혀 어려운 일이 아니다. ChatGPT는 인공지능 챗봇(AI chatbot)의 하나이다. 대형언어모형을 적용한 인공지능 챗봇에는 다양한 종류가 있다. 이 중에서 ChatGPT는 자동 회귀 모델링(autoregressive modeling)이라는 프로세스를 사용하여 응답을 생성한다. 이 모델은 이전에 나온 단어를 기반으로 다음 단어를 예측한다. [SEP] Justify:

Output: The text corroborates the claim by discussing the capabilities of ChatGPT, described as an AI chatbot. It implies that tools like ChatGPT can be used effectively by doctors, patients, and guardians, suggesting its potential role in patient engagement. The mention of its autoregressive modeling further supports the idea that it can generate meaningful interactions that could aid in preliminary diagnosis.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: In essence, the more positive the attitudes of green tourists towards virtual tourism, the more likely they are to express intentions to participate in such experiences. When green tourists harbor positive attitudes, such as perceiving virtual tourism as environmentally friendly, enjoyable, or valuable, it creates a favorable predisposition towards participating in virtual tourism. Therefore, cognitive factors and perceived factors of virtual tourism play a pivotal role in influencing the behavioral intentions of green tourists. As revealed in Table 2, green tourism cognition (β = 0. 146, P<0. 001), tourism value orientation cognition (β = 0. 472, P<0. 001), virtual tourism characteristics (β = 0. 258, P<0. 001), perceived benefits (β = 0. 132, P<0. 001), and perceived immersion (β = 0. 297, P<0. 01) all exert a positive impact on green tourists' behavioral intentions. Conversely, perceived risks (β=-0. 208, P<0. 001) demonstrate a negative influence. As a result, hypotheses H2a, H2b, H2c, H4a, H4b, H4c, and H5 were supported. 5. 4. 4 The relationship between experience type factors and green tourists' behavioral intentions The experience type factors, specifically the experience of natural scenery (β = 0. 357, P<0. 01) and the experience of human landscapes (β = 0. 221, P<0. 01), have a positively significant impact on green tourists' behavioral intentions towards virtual tourism experiences. Virtual experiences of natural landscapes captivate green tourists through the portrayal of nature's beauty, ecological diversity, and a dedicated commitment to environmental conservation. Simultaneously, virtual encounters with human landscapes, highlighting cultural, historical, and social facets, align with the expectations of green tourists seeking diverse and enriching cultural experiences. Consequently, these dual experiences craft compelling virtual tourism content, fostering positive attitudes and stimulating the behavioral intentions of green tourists. As a result, hypotheses H6a and H6b were supported. 5. 4. 5 The relationship between social situational factors and green tourists' behavioral intentions The indicators of industry policies (β = 0. 306, P<0. 001) and social standards (β = 0. 482, P<0. 001) within social situational factors positively influence the behavioral intentions of green tourists. [SEP] Justify:

Output: The text contrasts the claim by indicating that while positive attitudes of green tourists can lead to favorable predispositions towards virtual tourism, there are also perceived risks that demonstrate a negative influence on behavioral intentions (β=-0.208, P<0.001). This implication suggests that not all eco-tourism experiences are guaranteed to be positive for travelers, as perceived risks can deter participation.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 86), and race or ethnicity (Asian; Black; Hispanic; non-Hispanic White; multiethnic; self-reported Other/or Alaska Native/or other Pacific Islander; and unknown; P >. 99; Table 1). Across 10 runs, the multichannel T1- and T2-weighted network had the highest performance, with a mean absolute error (MAE) of 7. 7 weeks ± 1. 7 (all models, P =. 008; T1 vs T1 and T2, P =. 002; T2 vs T1 and T2, P =. 08; Fig 4A), ranging from 7. 5 weeks ± 5. 8 for those younger than 37 weeks to 14. 6 weeks ± 13. 4 for those older than 118 weeks (Fig 4B). The absolute error increased with increasing GCA (T1: r = 0. 35, P <. 001; T2: r = 0. 48, P <. 001; T1 and T2: r = 0. 34, P <. 001; linear regression t tests; Fig 4B). The T1-weighted network had an MAE of 9. 8 weeks ± 2. 3 (10. 0 weeks ± 7. 4 for those younger than 37 weeks to 16. 5 weeks ± 14. 7 for those older than 118 weeks), while the T2-weighted network had an MAE of 9. 1 weeks ± 1. 9 (5. 6 weeks ± 5. 2 at those younger than 37 weeks to 22. 5 weeks ± 12. 0 for those older than 118 weeks) (Fig E2 [online]). Overall, there was a strong positive correlation between true GCA and predicted GCA for all models (r = 0. 93 for T1- and T2-weighted model; Fig 4C; r = 0. 92 for T1-weighted model; r = 0. 91 for T2-weighted model) (Fig E2 [online]). Figure 4: Comparison of network performances based on sequence or sequences used and gestationally corrected age (GCA) (Kruskal-test: # = P <. 05, ## = P <. 01, ### = P <. 001; Mann-test: * = P <. 05, ** = P <. 01, *** = P <. 001). [SEP] Justify:

Output: The text discusses the performance of various models in predicting gestationally corrected age (GCA) based on T1- and T2-weighted networks, but it does not address the claim about advanced algorithms predicting emotions from brain scans. Therefore, it is irrelevant.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그러므로 정답을 유추하는 것이 아니라, 학습한 데이터에서 가장 적절한 단어를 순서대로 배열한다. 그러므로 대화형에 최적화되어 있고, 내용을 생성하는 데 유리하다. 또한 문맥은 사실 여부와 상관없이 매끈하고 그럴 듯하다. 이 논문에서는 인공지능 챗봇이 질병 진단 및 환자 치료에 대한 답을 할 때 얼마나 신뢰할 수 있는지, 또한 어떻게 해석하여야 하는지, 앞으로 어떤 방향으로 발전할지를 논의해 보고자 한다. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI chatbots can reliably provide answers regarding disease diagnosis and patient treatment. Additionally, it hints at the potential directions for the future development of these systems in healthcare, supporting the idea that chatbots can assist in medical contexts.


Input: Task: contrast the claim using the given text. Claim: Eco-tourism guarantees a positive experience for all travelers and communities involved. [SEP] Text: Favorable markers of industry policies and social standards play a pivotal role in cultivating trust and credibility among green tourists. Destinations that adhere to stringent industry policies and uphold elevated social standards garner heightened appeal among green tourists. The positive impact stemming from these factors augments the overall desirability of the destination, consequently elevating the probability that green tourists will manifest favorable behavioral intentions in terms of visiting and actively supporting such destinations. As a result, hypotheses H7a, H7b and H7c were supported. Conclusion and DiscussionIn the pursuit of sustainable tourism practices, green tourism emerges as a guiding principle for achieving harmonious coexistence between humanity and the environment. As our global community grapples with escalating resource constraints, the growing significance of virtual travel as a sustainable and environmentally friendly alternative becomes imperative, particularly for environmentally conscious travelers. This study, grounded in grounded theory and employing a comprehensive approach that integrates qualitative interviews with quantitative analysis, explores the factors shaping the behavioral intentions of Chinese green tourists within the domain of virtual tourism experiences. Our research findings indicate that various factors, including cognitive factors, perception factors, experience attitude, experience type factors, and social situational factors, can significantly influence Chinese green tourists' behavioral intentions towards virtual tourism experiences. This study not only provides a nuanced comprehension of the intricate dynamics guiding green tourists but also emphasizes the burgeoning importance of virtual tourism in the context of sustainable travel. By shedding light on the factors influencing behavioral intentions, our findings offer valuable insights for researchers and industry practitioners alike, aiming to enhance the appeal of virtual tourism experiences among environmentally conscious Chinese tourists. In a world delicately navigating the balance between tourism and environmental preservation, this research contributes significantly to the ongoing discourse on sustainable tourism and holds the potential to reshape the travel landscape towards a more ecologically sustainable future. DeclarationsAuthor Contribution(LW) was responsible for conceptualizing and designing the study, leading the data collection process, and writing the initial draft of the manuscript. LW also handled the initial statistical analysis and data set preparation. (ZW) participated in the detailed discussion of the research design, primarily handled further data analysis using advanced statistical methods, and made significant contributions to the literature review section. (XH), as the project coordinator, oversaw the progress of the research, ensuring the scientific rigor and precision of the methods used. XH deeply analyzed the methodology and results, suggesting improvements, and was responsible for writing and revising the methodology and discussion sections to ensure the overall quality of the research. [SEP] Justify:

Output: The text contrasts the claim by indicating that while favorable policies and social standards may enhance the appeal of eco-tourism destinations, it does not guarantee a positive experience for every traveler or community involved. It discusses how various cognitive and situational factors can influence the behavioral intentions of tourists, suggesting that not all experiences are inherently positive or guaranteed.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: (A) Overall distributions of T1-, T2-, and T1- and T2-weighted networks absolute errors over 10 iterations of each model. (B) Comparison of network errors for subsets of GCAs. (C) Scatterplot shows true corrected gestational age versus predicted corrected gestational age from the best performing multichannel T1- and T2-weighted network over 10 runs. linear correlation is 0. 93. Figure 4:Download as the best-performing multichannel T1- and T2-weighted network, average attention maps for brains in the GCA of less than 40 weeks and the GCA of more than 120 weeks bins reflected attention in different locations of the brain (Fig 5). Specifically, in the less than 40 weeks bin, the network was more attentive to the cerebellum, deep gray nuclei, and occipital white matter. In the more than 120 weeks bin, the network was more attentive to the anterior supratentorial white matter and centrum semiovale. Figure 5: Average attention maps from the multichannel (T1- and T2-weighted) model show that the network is more attentive to the cerebellum, deep gray nuclei, and posterior supratentorial white matter in the younger age group and the anterior supratentorial white matter and centrum semiovale in the older age group, which corresponds with the known progression of myelination. (A) Overlay of maps of infants (>120 weeks, red) and neonates (<40 weeks, blue), with overlap subtracted and a threshold of 0. 8 for each age group. (B) Attention map of neonates (<40 weeks) only, without thresholding. (C) Attention map of infants (>120 weeks) only, without thresholding. Figure 5:Download as demonstrate generalizability of our GCA estimation approach, we tested our models on an external test set of normal brain MRI scans from the study (Table E1 [online]). Age distribution at the time of MRI for the 226 neonates and infants from the NIH test set is represented in Figure E3 (online). Similar to the internal data set, absolute error was positively correlated with GCA (T1: r = 0. 66, P <. 001; T2: r = 0. 47, P <. 001; T1 and T2: r = 0. [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on the performance of multichannel T1- and T2-weighted networks in relation to brain MRI scans of infants and does not mention any algorithms predicting emotions or discuss emotional analysis based on brain scans.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: ChatGPT의 의학 지식에 대한 답은 어느 수준인가? PubMed ( ChatGPT를 검색하여 그 가운데 의료 지식을 점검한 논문 내용은 다음과 같다. 한림대학교 의과대학 학생과 같은 기생충학 시험 문항으로 점검한 결과, ChatGPT는 79문항 중 48문항(60. 8%)에 적절한 답을 하였고, 의대생 77명은 평균 71. 8문항(90. 8%)에 적절한 답을 하였다. 또한 정답인 48문항 중 7문항은 설명에 수정이 필요하였다[2]. Examination (USMLE) 300문항을 ChatGPT가 시험을 치르도록 하였을 때 합격선 60%에 다다랐다[3]. [SEP] Justify:

Output: The text corroborates the claim by presenting evidence that ChatGPT was able to answer a considerable percentage of medical questions correctly, indicating its potential capability to assist in medical diagnosis. Specifically, it states that ChatGPT provided appropriate answers to 60.8% of 79 medical questions analyzed, which shows its utility in preliminary assessments relevant to patient engagement.


Input: Task: pass the claim using the given text. Claim: Blockchain is the underlying technology that enables cryptocurrencies. [SEP] Text: Clearly, if the government backs its currency by forbidding the usage of other currencies (in its geographical territory), then it enforces a certain minimum value for the currency. However, not all governments can or do that. In most countries, the population can make economic transactions with many currencies. Most governments back their currencies by enforcing tax payment with them. Similarly, the governments pay their employees with the national currency. However, these conducts are equivalent to the presence of a rich man in a market, who declares that any economic transaction with him can take place only with a certain currency. As richer this man is, the higher will be the value of this currency. Clearly, a government is equivalent to a very rich man, but the differences are quantitative rather than qualitative ones. One of the most instructive examples in this regard is the Swiss dinars in northern Iraq during the gulf war [25]. This is a very interesting example, which vividly illustrates the fact that paper money can be used as a medium of exchange, despite the fact that it is backed neither by any commodity nor by any government. 13. Dependence on the current monetary system An argument against Bitcoin, which was very common in its early years, is that Bitcoin is not a real medium of exchange because most traders, which accept Bitcoin, convert them to fiat currency almost immediately after the transaction. Nowadays, this argument is heard less since many sellers prefer Bitcoin over fiat money. They prefer spending their fiat money and receive and keep their cryptos. Moreover, nowadays many employees are paid in cryptocurrency. However, it is important to stress that whether the users of Bitcoin prefer to make an additional transaction after selling a product, that is, to convert the crypto to fiat, is irrelevant to the validity of Bitcoin as a medium of exchange. First, there is no praxeological difference between the exchange of Bitcoin with fiat and the exchange of Bitcoin with other goods. If a seller prefers to convert Bitcoin into fiat currencies, it only means that he had decided to use Bitcoin as a medium of exchange to purchase the fiat currencies. Moreover, one should not ignore the fact that the main reason that sellers prefer to convert their cryptos back into fiat is due to government regulations (mainly taxation) and has nothing to do with the fundamental monetary properties of Bitcoin. 14. Bitcoin and economic calculations economists used the volatility problem and the last argument to claim that Bitcoin cannot be used for economic calculations. [SEP] Justify:

Output: The text does not specifically address or provide information about blockchain technology. Instead, it discusses the use of Bitcoin as a medium of exchange, the dependence on government-backed currencies, and the influence of regulations on cryptos. Therefore, it does not corroborate or contrast with the claim about blockchain technology enabling cryptocurrencies but is irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 57, P <. 001; linear regression t tests). We used the data set GCA and found the average MAE across 10 runs of each model was 10. 6 weeks ± 10. 0 (5. 2 weeks ± 2. 1 at <42 weeks to 24. 8 weeks ± 13. 6 at >118 weeks), 7. 6 weeks ± 8. 5 (4. 5 weeks ± 2. 6 at <42 weeks to 17. 8 weeks ± 16. 0 at >118 weeks), and 9. 1 weeks ± 7. 8 (2. 1 weeks ± 0. 7 at <42 weeks to 19. 2 weeks ± 12. 4 at >118 weeks) for T1-, T2-, and T1- and T2-weighted models, respectively (Fig 6A–6C). Figure 6: Scatterplots of true corrected gestational age versus predicted corrected gestational age from the of Health (NIH) Pediatric MRI data set (n = 226). (A) Predictions from each run of the T1-weighted model trained on our institutional data were averaged. correlation coefficient was 0. 89, and mean absolute error (MAE) was 10. 6 weeks ± 10 (SD). (B) Predictions from each run of the T2-weighted model trained on our institutional data were averaged. correlation coefficient was 0. 91, and MAE was 7. 6 weeks ± 8. 5. (C) Predictions from each run of the T1- and T2-weighted multichannel model trained on our institutional data were averaged. correlation coefficient was 0. 90, and MAE was 9. 1 weeks ± 7. 8. (D) Predictions of the multichannel model from the institutional data that were further fine-tuned on the NIH data set. correlation coefficient was 0. 94, and MAE was 5. 9 weeks ± 5. 9. GCA = gestationally corrected age. Figure 6:Download as Powermodels had lower performance with a higher MAE on the external test set compared with the internal test set (T1: internal, 9. 8 weeks vs external, 10. 6 weeks; P <. 001; T2: internal, 9. 1 weeks vs external, 7. 6 weeks; P =. 030; T1 and T2: internal, 7. 7 weeks vs external, 9. [SEP] Justify:

Output: The text does not address or relate to the claim regarding advanced algorithms predicting emotions based on brain scans. Instead, it discusses the performance of predictive models used for gestational age determination based on MRI data. Therefore, this text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 미국의 33명 의사가 출제한 284문항의 질문을 ChatGPT가 답을 하였을 때, 정확도는 1-6점 Likert scale에서 평균 4. 8, 완결성은 1-3 scale에서 평균 2. 5였다[4]. 미국 내과 전문의가 제작한 흔한 증상으로 구성한 10개 증례보고에서 ChatGPT가 제시한 감별진단명 목록의 정확도를 점검한 결과, 두 명의 의사가 제안한 감별진단 목록 정확도는 98. 3%인데 반하여 ChatGPT는 83. 3%였다(P=0. 03) [5]. 실제 증례에서 진단 내릴 수 있을까? [SEP] Justify:

Output: The text corroborates the claim by providing evidence of ChatGPT's performance in a diagnostic context. It mentions that ChatGPT attempted to answer medical questions presented by physicians and achieved an average accuracy of 83.3% in differential diagnosis, demonstrating its potential as a tool for preliminary assessments, which aligns with the claim about AI chatbots assisting in diagnosis.


Input: Task: pass the claim using the given text. Claim: Blockchain is the underlying technology that enables cryptocurrencies. [SEP] Text: would instead like to draw your attention, dear colleagues, to the fact that while we were focused on alleviating risks — on protecting consumers and financial stability, all laudable goals — we inadvertently created CBDC! A new version, that is, which we call “synthetic CBDC. ”Yes, if eMoney providers can keep client funds as central bank reserves, and if these are protected from other creditors, then, by proxy, eMoney users can hold, and transact in, a central bank liability. Isn’t that the very definition of CBDC? Synthetic CBDC has notable advantages relative to the full-fledged version from the previous presentation, in which the central bank creates tokens or offers accounts to the public. Synthetic CDBC outsources several steps to the private sector: technology choices, customer management, customer screening and monitoring including for “Customer” and AML/CFT (Anti-and Combating the Financing of Terrorism) purposes, regulatory compliance, and data management — all sources of substantial costs and risks. The central bank merely remains responsible for settlement between trust accounts, and for regulation and close supervision including eMoney issuance. If done appropriately, it would never need to lend to eMoney providers, as their liabilities would be fully covered by reserves. A synthetic CBDC is essentially a public-private partnership that encourages competition between eMoney providers and preserves comparative advantages. The private sector concentrates on innovation, interface design, and client management. And the public sector remains focused on underpinning trust. Open-economy complicationsCheck-mark? Have we finished work and can now stroll along the inviting Mythenquai just outside? Not so fast. Things get a little more complicated as we consider cross-border payments. Clearly, eMoney could bring enormous benefits to cross-border payments, which currently tend to be slow, opaque, and expensive. The token version of eMoney could also facilitate cash payments in cross-border financial trades once assets migrate to the blockchain (allowing seamless “delivery versus payment”). But these gains must be weighed against risks. An example may help. The case of a person in Zurich, transferring Swiss franc eMoney to a friend in, say, Italy, is simple. Marlis clicks a button, and Francesco gets the funds. But suppose Francesco wants to receive euro-based eMoney. [SEP] Justify:

Output: The text does not directly address the claim regarding blockchain and its role in enabling cryptocurrencies. Instead, it discusses synthetic CBDCs, eMoney providers, and the implications for cross-border payments, which are related to digital currencies but do not specifically support or contrast with the claim about blockchain's foundational role for cryptocurrencies.