Input: Task: pass the claim using the given text. Claim: Genetically modified trees could complement natural reforestation efforts. [SEP] Text: However, in a second study by Jepson and Arakelyan (2017b) of the general public, the majority of study participants considered GM trees and food crops equally acceptable or unacceptable. Taken together, these two studies indicate that perceptions to the use of GM trees versus agronomic crops may vary by population. More specifically for tree-related perceptions, Hajjar et al. (2014) evaluated acceptance of six different forest management strategies in response to climate change by the general public and community leaders in western Canada. In general, the researchers found decreasing acceptance by both the public and community leaders of strategies that involved more manipulation. However, while the strategy of using GM trees had the second lowest level of acceptance, it was still higher than the strategy of no intervention at all. This aligns with the findings of Jepson and Arakelyan (2017a, b), which presented a range of possible solutions to ash dieback in the UK, including the use of GM native ash (Fraxinus L. species) trees with increased disease tolerance. While the use of GM native trees was generally ranked low compared to more conventional planting and breeding strategies, respondents still preferred the use of GM trees over a strategy of no action. Acceptance towards the use of hybrids of native and non-native ash trees was also evaluated in these studies. The hybrid strategy was viewed more favorably than the use of GM trees, but not as highly as conventional breeding and re-planting methods. Perceptions of tree and forest biotechnology have been found to vary by demographic variables, such as age (Jepson and Arakelyan (2017a, b; St-Laurent et al. 2018) and gender (Jepson and Arakelyan 2017a), while the concepts of nativeness and naturalness have also been found to be important in shaping these perceptions (Hajjar and Kozak 2015; Jepson and Arakelyan 2017a). These studies have begun the process of unravelling the complex perceptions towards hybrid and GM tree use, but more remains to be uncovered, especially in direct comparisons of hybrid and GM tree perceptions and the perceived concerns or benefits driving them. It is also essential to investigate perceptions in specific populations, beyond the ‘general public’, as this approach may limit the ability to understand the complex motivations of different segments of the population (Fischhoff and Fischhoff 2001). [SEP] Justify:

Output: The text discusses the public perceptions and acceptance of genetically modified (GM) trees in contrast to natural reforestation efforts. While it mentions that GM trees are preferred over no action at all, it primarily delves into the varying acceptance levels and perceptions without directly addressing how GM trees might complement natural reforestation efforts. Therefore, the text does not support or refute the claim but rather provides an exploration of public opinion, making it irrelevant to the specific claim.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Как часто появляется; Приложите скриншот (снимок экрана) с ошибкой, если это возможно. Объем отправляемого сообщения, включая все приложения к нему, не должен превышать 15 Мб. В противном случае ваши письма будут отсечены системой; Укажите адрес домена, на котором вы работаете. Как не надо писать обращения, такие обращения рассматриваются в последнюю очередь: «Ничего не работает! Срочно почините! »; «У меня тут программа раньше работала, а потом перестала. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not address or provide information about sea turtles or their navigation methods. It discusses unrelated instructions and errors regarding communication.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: Accuracy histogram of CNN, Librosa and PyAudio for all users Figure 8 demonstrates that for users who prefer the genre of music, CNN and Librosa outperform PyAudio. This indicates that both methods highlight the genre parameter and are genre-based. Figure 9 indicates that for users who prefer artists, CNN and Librosa are also the top performers, because there is a strong correlation between the artist and the genre of music. In the last group of users, there is a noticeable improvement for PyAudio, but CNN still remains the best. This reflects that the CNN based strategy is superior for taste-based recommender systems. In summary, the strong performance of CNN and Librosa for users who prefer genre and artist-based music recommendations can be attributed to their ability to capture relevant audio features and recognize semantic information and stylistic elements in the music. These results highlight the importance of feature extraction and the suitability of CNN and Librosa for music recommendation tasks that involve genre and artist p6. Our music recommender interface has been developed with python. Figure 10 depicts the overall interface. 10. png Figure 10. The recommender system interface After selecting the strategy used, the result is provided in the following Figure 11. 11. png Figure 11. Recommender music list interface 7. conclusion, our research has highlighted the importance of considering not only the textual aspects but also the rich audio characteristics of music in the development of music recommendation systems. By adopting a multi-model approach that emphasizes feature extraction and selection, incorporating Librosa, PyAudio analyses, and CNNs, we have demonstrated the potential for enhancing the personalisation of music recommendations. Our experiments on the GTZAN dataset, encompassing 10 distinct music genres, have revealed valuable insights into the effectiveness of different approaches. Our findings indicate that CNNs are a reliable choice for crafting personalized music recommendations, particularly for users with p1388. [2] Cheng, Z. , Shen, J. (2016). On effective location-aware music recommendation. ACM Transactions on (TOIS), 34(2): 1-32. [3] Wang, D. , Deng, S. , Zhang, X. , Xu, G. (2018). Learning to embed music and metadata for context-aware music recommendation. Web, 21: 1399-1423. [4] Aghdam, M. H. (2019). [SEP] Justify:

Output: The text supports the claim by discussing how CNN and Librosa outperform other methods in music recommendation systems, indicating their ability to capture relevant audio features and enhance personalization. Specifically, it mentions the effectiveness of these methods for users who prefer genre and artist-based recommendations, highlighting a focus on learning user preferences to improve the accuracy of recommendations.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees could complement natural reforestation efforts. [SEP] Text: Ecological concerns about GM trees were consistently stronger than economic concerns (Fig. 3b). The greatest concern about GM trees overall was the potential for invasiveness (69% strongly concerned). The greatest economic concern about GM trees was the lack of performance knowledge (36% strongly concerned). Low availability was the least important economic concern (54% not concerned). As with hybrid trees, the only land manager characteristic and perception variable significantly associated with both ecological and economic concerns about GM trees was the level of agreement with tree improvement advantages, with the direction of the relationship depending on the type of concern (Table 6). A higher level of ecological concerns about GM trees was associated with less agreement with GM trees having tree improvement advantages (χ2 = 5. 454; p = 0. 020). Conversely, a higher level of economic concerns was associated with greater agreement with GM trees having tree improvement advantages (χ2 = 20. 415; p < 0. 0001). The level of economic concerns about GM trees was positively associated with age, with concern increasing with age (χ2 = 9. 958; p = 0. 041). The level of ecological concerns was not associated with the level of economic concerns about GM trees (see Table 6), nor any land manager characteristics (i. e. , gender, education, region, organization type, land type managed) (see Table 6 for all statistical values of non-significant relationships). Attitudes towards using GM trees for different purposesOverall, there was more agreement than disagreement for using GM trees for various proposes (Fig. 4). There was greatest agreement (agree or strongly agree) among land managers with using GM trees for “conservation and restoration of at-risk species” (56%), “timber production” (54%), and “non-timber production” (47%). The purposes for using GM trees with the greatest disagreement (strongly disagree or disagree) were “promoting biodiversity” (39%), “reforestation and restoration of forests” (34%), and “promoting wildlife habitat” (33%). Fig. 4Survey respondents’ attitudes towards using GM trees for various purposes, in order of decreasing agreement (n = 253)Full size imageAs with hybrid trees, two land manager characteristics were associated with attitudes towards using GM trees for different purposes. [SEP] Justify:

Output: The text contrasts the claim by revealing that there is significant disagreement among land managers regarding the use of GM trees for reforestation purposes. Specifically, it states that there was a degree of disagreement with using GM trees for 'reforestation and restoration of forests' (34% disagree) and 'promoting wildlife habitat' (33% disagree). This suggests that despite some agreement for certain uses, many land managers do not view GM trees as a complementary approach to natural reforestation, highlighting concerns about ecological ramifications.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Когда снова заработает? »; «Я не могу работать. Позвоните мне! ». Сколько обращений надо написать? Необходимо придерживаться правила «одна ошибка — один запрос в поддержку». Если у вас две разных проблемы — «поправить контент» и «разместить свежий выпуск журнала» — сделайте две заявки. Нельзя создавать несколько обращений, посвященных одной проблеме чаще, чем раз в два часа. Повторные заявки замедляют работу техподдержки. Время обработки заявки? [SEP] Justify:

Output: The text does not relate to the claim about sea turtles and navigation. Instead, it discusses issues related to customer support and request protocols, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: As the day goes on, the assistant informs Henry that his grandmother is unwell and advises him to buy medicine. When leaving the house, the DA reminds Henry to take his key. When the postal worker rings at the door, Henry allows the assistant to disable the alarm system and open the door for sixty seconds. The DA warns that the dryer might have a malfunction and suggests sending diagnostic data to the maintenance service. 2. Descriptive text: After the video clip, descriptive text informed the user that the DA needs personal data to provide such functionalities. 3. Introduction of different privacy features: We introduced several versions of the DA that differ with regard to the design of privacy features. In the next step, we explained these design features and the three identified key attributes. We provided a description and examples for each attribute level. 4. Users start choice-based conjoint survey: Table 2 shows an example of a choice set. Table 2 Example of a CBC choice set with user’s marker on product 3Full size table“Customer definition”: Analyzing demographic and psychographic informationIn the “customer definition” stage (Chapman et al. 2008), we enlisted the help of a market research company to investigate a sample of the German population that is representative with regard to demographics, specifically gender and age distribution. We predefined a sample size of 300 participants, as this is a common number for representative user studies in this domain (e. g. Gensler et al. 2012). We elicited demographic (e. g. age, education, etc. ) and psychographic (e. g. risk tolerance, technology affinity and playfulness) information. This is needed to better understand users’ decisions and to identify consumer groups as a basis for further market segmentation. To do so, we applied established scales to the evaluation of our psychographic variables, such as Steenkamp and Gielens (2003) for the users’ attitude towards innovation, Meuter et al. (2005) for measuring the participants’ technology affinity, and Taylor (2003) and Westin (1967) for examining privacy concerns. The interaction of the user, originally from Serenko and Turel (2007), was adapted to the usage of DAs and showed similar reliability scores. [SEP] Justify:

Output: The text corroborates the claim by illustrating how the assistant interacts with Henry, learning about his needs and preferences through various scenarios. For instance, it reminds him about taking his key and suggests sending diagnostic data based on potential issues identified with household items. These functions imply that the assistant is utilizing personal data to better tailor its recommendations to Henry's unique context, indicating an ability to learn user preferences.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees could complement natural reforestation efforts. [SEP] Text: Respondents managing urban land were less likely to agree with using GM trees for restoration purposes (χ2 = 4. 646; p = 0. 035), but type of land managed was not associated with agreement for other purposes (see Table 6). Age was only associated with agreement with using GM trees for cultural ecosystem services, with agreement increasing with age (χ2 = 4. 792; p = 0. 029). Neither gender, education, region of residency in Indiana, nor organization type was associated with agreement for using GM trees for any purposes (see Table 6). In addition, agreement with the tree improvement advantages of GM trees was strongly and positively associated with agreement with using GM trees for all five purposes: restoration (χ2 = 85. 111; p < 0. 0001), provisioning ecosystem services (χ2 = 70. 992; p < 0. 0001), supporting ecosystem services (χ2 = 73. 369; p < 0. 0001), regulating ecosystem services (χ2 = 61. 989; p < 0. 0001), and cultural ecosystem services (χ2 = 47. 996; p < 0. 0001) (Table 6). There was also a positive relationship between land managers’ level of economic concerns and their agreement with using GM trees for restoration (χ2 = 5. 936; p = 0. 015), provisioning ecosystem services (χ2 = 5. 596; p = 0. 018), supporting ecosystem services (χ2 = 5. 138; p = 0. 023), and regulating ecosystem services (χ2 = 8. 002; p = 0. 005), but not for cultural ecosystem services (χ2 = 3. 418; p = 0. 065). Conversely, level of ecological concern was negatively associated with agreement with using GM trees for restoration (χ2 = 9. 099; p = 0. 003), provisioning ecosystem services (χ2 = 10. 760; p = 0. 001), and supporting ecosystem services (χ2 = 12. 328; p < 0. 0001), but not for regulating ecosystem services (χ2 = 1. 989; p = 0. 158) and cultural ecosystem services (χ2 = 0. 304; p = 0. 581). [SEP] Justify:

Output: The text contrasts the claim by indicating that there is significant disagreement among land managers regarding the use of genetically modified (GM) trees for restoration purposes. Specifically, urban land respondents were less likely to agree with using GM trees for restoration efforts, highlighting a skepticism that contradicts the idea that GM trees might serve as a complement to natural reforestation efforts. The text also notes that ecological concerns negatively impacted agreement with using GM trees for restoration, further challenging the claim.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Задания выполняются в порядке живой очереди, обычно выполнение работ занимает от нескольких часов до трех-четырех дней. Пожалуйста, не пишите в выполненные и закрытые задания, даже если это слово «Спасибо». Нам очень приятно, но это вносит беспорядок в систему отчетов и управления заданиями. Пожалуйста, четко формулируйте свой вопрос или задание. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the order of task completion and does not provide any information related to sea turtles or their navigation methods.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: We find that users generally opt for a restricted set of explanations, but maximum gamification of privacy in the context of digital assistants. Our customer clustering further shows that this preference for maximum gamification is specific to “Best Agers”, who do not value digital assistants as much. “DA Advocates” generally prefer restricted gamification – but also prefer this to no gamification. Strong use of gamification therefore seems appropriate when introducing digital assistants to older users who might not use them otherwise. This might be especially true in scenarios such as ambient assisted living. Based on our findings, we encourage providers of such services to leverage gamification to improve their customers’ interaction with digital assistants and other smart services. This finding also puts the initial ranking across all users into perspective: For mainstream assistants, selective gamification may be the best option rather than a full-on serious game. In order to cater to both user clusters, a serious game could be offered independently, with an entry point in the assistant to gamified privacy functionality. Further analysis of how these clusters differ may also lead to a more differentiated view of end-users’ privacy concerns about assistant systems (Knote et al. 2019). These concerns have been shown to be very relevant to users’ potential willingness to pay and adoption of DA in various scenarios (Brill et al. 2019; Mihale-Wilson et al. 2017; Zibuschka et al. 2019). Our findings show that at least restricted privacy gamification and explainability of the underlying artificial intelligence are worth pursuing, as they have business relevance and can significantly increase users’ willingness to pay for digital assistants. This finding might well translate into other smart services, e. g. on the Internet of Things, as well. This suggests the need for further research and development in these areas, as it is far from clear how these features can be realized in practice. LimitationsAlthough we tried to ensure a high quality of our user study, there are some limitations to our work. First, our literature review does not claim to be mutually exclusive or collectively exhaustive and might omit discussions, e. g. about cultural embedded privacy concerns even within the same legal framework. This may influence the key attributes. This also applies to the final set of key attributes selected by the expert panel. An end-user perspective of features is not included in the study refinement stage. [SEP] Justify:

Output: The text corroborates the claim by indicating that understanding user preferences, particularly through gamification of privacy, can enhance user interaction with digital assistants. It emphasizes the importance of tailoring features to different user clusters, which implies that AI assistants are capable of adapting and learning user preferences to provide better recommendations. Additionally, the findings suggest pursuing explainability in AI, which aligns with improving user experience and meeting user needs, further supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees could complement natural reforestation efforts. [SEP] Text: Perceived nativity of GM treesForty-seven percent of respondents perceived a GM version of a native species as not being native, while 26% said that it would still be native and 27% reported “don’t know”. Respondents with higher levels of ecological concerns about GM trees were more likely to perceive a GM version of a native species as not native, while those with lower levels of ecological concerns were more likely to perceive it as native (Fisher’s exact p < 0. 0001). Further, respondents with stronger agreement with the tree improvement advantages of GM trees were more likely to perceive a GM version of a native species as still being native (χ2 = 38. 488; p < 0. 0001). No other land manager characteristics or perceptions were associated with perceived nativity of GM trees (for the full statistical values see 1. Introduction Criterion: whether human activity indirectly or directly introduced a species to the area 2. Criterion: whether a species originally evolved in the area 3. Range Criterion: whether a species has historically grown in the area 4. Criterion: whether a species degrades and harms the environment of the area 5. Membership Criterion: whether a species is an integrated component of the community or ecosystem of the area Woods and Moriarity (2001) are clear to state that meeting or not meeting any single criterion does not automatically classify a species as being native or non-native, but rather how a species meets the most applicable cluster of these criteria given its specific circumstance. To explore this situation for hybrid and GM trees, it might be helpful to consider the perception of nativeness of these tree types on a spectrum. On the more native end, humans are simply modifying a pre-existing species so that the majority of it will still be the same as the unhybridized version, just slightly improved so that it can survive in its changed environment. If viewed from this perspective, hybrid and GM trees would probably meet criteria 1, 2, 3, and 5. Conversely, on the other extreme, the non-native end, simply by the fact that humans intervened to combine part of one species with another to form hybrid and GM trees, we are essentially creating entirely new species—that have no ‘track record’ in the target restoration area. If viewed from this second perspective, these tree types would certainly not meet criteria 1, 2, 3, and 5 for nativeness. [SEP] Justify:

Output: The text contrasts the claim by providing a nuanced discussion of the perception of GM trees and their nativeness. It highlights that there are significant concerns regarding the introduction of GM trees based on their perceived non-nativeness, suggesting that many respondents believe that GM trees do not belong in natural reforestation efforts. The text elaborates on the criteria used to assess nativeness, emphasizing that while some perceive GM trees favorably, others see their creation as harmful, questioning their compatibility with natural reforestation efforts. Therefore, the text implies that GM trees may not complement reforestation as intended due to these contrasting perceptions and ecological concerns.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Сотрудники службы зачастую незамедлительно берут задание в работу, и поэтому многочисленные корректировки ведут как минимум к замедлению работ, а как максимум — к ошибкам. Куда сообщать о технических проблемах? написать письмо на support@editorum. ru, заполнить форму обращения в техподдержку, написать нам в онлайн-чат. Мы перезвоним вам Инструкции Вы можете ознакомиться с инструкцией по работе с системой. Руководство пользователя (docx) × you sure you want to delete? [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses operational issues and technical support without any mention of sea turtles or their navigation methods.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: 1 IntroductionAccording to the Organization (WHO), it is estimated that the world population consisting of older people will reach 22% by 2050 (da Paixão Pinto et al. 2021), with most older people living alone in their homes (O'Brien et al. 2020). Mobility inaccessibility, falls, injuries, and accidents are risks faced by the older people (Older people defined here as those aged ≥ 65 years and older (between the age of 65–74, 75 and older)) (Gudala et al. 2022). Digitalization employing (AI) driven techniques can support mobility accessibility to be safe. Voice-assistants may be one type of AI technology that can improve the safety of older adults in urban environment (da Paixão Pinto et al. 2021). AI-based voice-assistants uses Processing (NLP) to communicate with users, (ML), and speech recognition to process requests from users. Therefore, AI-based voice assistants have been adopted to improve tasks such as self-management in healthcare for older people (Capodieci et al. 2018). Also, the deployment of AI allows older people to speak to and receive in-context responses from a computer system similar to humans to human interactions (McLean and Osei-Frimpong 2019). Even though the technological and digital knowledge of older people are improving, some physiological changes occurs as we get older such as decrease in coordination linked to vision, hearing, and sensitivity of finger and hand. These AI-based voice assistants are programmed to provide responses when receiving questions from individuals. As they are in constant learning process through the experience acquired by interactions with datasets or users (da Paixão Pinto et al. 2021). By using AI-based voice-assistants’ older people do not need to always use the interface, mouse, or keyboards to gather information on mobility and navigation when they go walking; instead, they can ask the AI-based voice-assistants for available and accessible mobility modes and receive the information as recommendations verbally via an audible interface (O'Brien et al. 2020). AI-based voice-assistants are still a new technology and the use of this technology by older people is still in an investigative phase. Thus, there is need to identify user and system design requirements needed for successful implementation (Arnold et al. 2022). [SEP] Justify:

Output: The text corroborates the claim by discussing how AI-based voice assistants learn through interactions with users, stating that they are in a 'constant learning process through the experience acquired by interactions with datasets or users'(da Paixão Pinto et al. 2021). This indicates that the technology is indeed able to learn user preferences and improve the recommendations it provides, which supports the idea presented in the claim.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees could complement natural reforestation efforts. [SEP] Text: From either the more native or non-native perspective, it is still unclear whether hybrid and GM trees of at-risk native species fall in or outside of the fourth criterion pertaining to degradation. As discussed previously, unintentional hybridization of native with non-native tree species has been documented in many instances as being ecologically detrimental (e. g. , Burgess and Husband 2006; Culley and Hardiman 2007; Zalapa et al. 2009). However, whether that will be the case for the hybrid and GM trees currently in development specifically for species restoration, rather than as an unintended, uncontrolled consequence (i. e. , escape from cultivation), is still largely unknown. Further, the goal of using either hybrid or GM trees for restoration is actually to support ecosystems through reintroduction of a declining species, thus meeting the fourth criterion – but again, whether that goal will be met or not is still to be determined. In our study, land managers expressed strong ecological concerns about hybrid and GM trees which were also associated with their perceptions of nativeness. Thus, to aid in nativeness determinations, more research is needed on the ecological consequences to using hybrid and GM trees developed specifically for restoration and particularly whether they degrade or support their ecosystem. ConclusionThe goal of this research was not to make a judgement as to whether hybrid or GM trees should be used. What it does is provide insights into who Indiana land managers are, their perceptions to these tree types, and thus how researchers could engage with land managers in building and acting on recommendations on hybrid and GM tree use. Respondents’ strongest concern with these tree types was overwhelmingly about the potential for invasiveness, and all ecological concerns were stronger than economic concerns. Respondents agreed most with using hybrid and GM trees for conservation and restoration of at-risk species, timber production, and non-timber products (fruit, syrup, etc. ) and disagreed most with using them for promoting biodiversity and reforestation and restoration of forests. We also found that perceptions were not uniform and varied by several land manager characteristics, such as their concerns, age, and type of land managed. While greater ecological concern was negatively associated with current hybrid use, economic concerns did not significantly predict use. Based on the differences seen in this study between urban and natural land managers, further investigation is also required to understand how a land manager’s specific work purpose shapes their perceptions and use of hybrids and GM trees. [SEP] Justify:

Output: The text contrasts the claim by highlighting concerns among land managers regarding the use of hybrid and GM trees in restoration efforts. It emphasizes that these tree types are met with skepticism due to potential ecological detriment and invasiveness, noting that while some land managers agree with their use for the conservation of at-risk species, they overwhelmingly disagree with their use for promoting biodiversity and reforestation. This indicates that there is significant apprehension surrounding the integration of genetically modified trees into natural reforestation efforts.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: of Geosciences > Vol. 8 No. 8, August 2017 of Earth’s the Earth’s (). DOI: 10. 4236/ijg. 2017. 88059 1,087 Downloads 3,426 Views 1071. doi: 10. 4236/ijg. 2017. 88059. 1. the present time, the most elaborate theory of substantiating electrical currents generation in the planet’s metal core is based on hydromagnetic dynamo which works on the self-excitation principle. There are a lot of theoretical and experimental works to prove this model [1] - [6]. However, such model, able not only generate but also fully maintain the Earth’s magnetic field for a long while, needs some conditions to be met, such as low viscosity of the core metal fluid, convective vertical movements in it, raw magnetic field and so on. Besides, based on the browsed professional reviews and works [2] - [9] it is obvious that there is still no fundamental and effective principle of generating the Earth’s dipole magnetic field for the hydromagnetic dynamo model. Nevertheless, even in the first half of the last century there were attempts to explain the Earth’s magnetic field with thermoelectromotive forces existing in the core, which are conditioned by turbulent convective movements of the core’s liquid [10] [11] [12]. However, T. Rikitaki [1] considered those attempts as unlikely thermoelectrical effects, explaining this by absence of distinctness of the core’s thermoelectrical power. T. Rikitaki’s opinion turned out to be conclusive, since then all the researches of thermoelectrical effects were not taken into consideration in the dynamo theory. There is another reminder of thermoelectrical currents’ role in forming magnetic fields of hot CP stars and, probably, planets in a work of A. Z. [SEP] Justify:

Output: The text does not address the claim about sea turtles navigating using the Earth’s magnetic field. Instead, it focuses on the theory of the Earth’s magnetic field generation and hydromagnetic dynamo processes without mentioning sea turtles or their navigation.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: Similarly, the deployment of AI-based voice-assistants in real world applications is challenging due to design requirements. Also, these AI based systems have to understand human languages and learn from communications, increasing its knowledgebase (Tascini 2018), in some cases, it has to remotely connect with family members, care givers, and emergency department when older people fall. In addition, existing technological tools provided to the aging population fail to consider the changing needs of older people, for example by avoiding small font sizes and use of poor color contrast on user interfaces (Wedberg and Dalgren 2021). Thus, older people face several techno–social challenges that influences the use of this technology across aging population. As such an architectural model-based approach is required to fill the current knowledge gaps regarding the AI techniques needed to support the user-centered implementation of AI-based voice-assistants. Therefore, this 2. 1 Background of AI-based voice-assistantsVoice-assistants are automated applications that enables human-to-computer interaction via text or natural language (García-Méndez et al. 2021). Voice-assistants are programmed to reply to certain inputs thereby resulting to user feedback (Tascini 2018). They are able to recognize natural language and continuously learn from interactions, becoming progressively intelligent (Tascini 2019). A common method to voice-assistant development is to pre-program questions and phrases to respond to individuals input in the form of words, phrases, and questions. Additional contents are routinely added to improve the knowledge on the dialogues held (García-Méndez et al. 2021). One of the strengths of voice-assistants is their capability to be always active and to be self-contained to provide individuals with responses and help. In general, voice-assistants have three key components which comprises of a user interface, a normaliser, and a knowledge base. Recently AI-based voice-assistants has surfaced which has the ability to create conversation between a digital agent and a user. As such, voice-assistant have been progressed through easy standalone intelligent home devices and application integrated in smart phones (e. g. , Microsoft's etc. ) (Wiratunga et al. 2020). [SEP] Justify:

Output: The text corroborates the claim by stating that AI-based voice-assistants 'continuously learn from interactions, becoming progressively intelligent' (Tascini 2019). This suggests that these systems adapt over time, likely influencing their ability to learn user preferences and improve their recommendations.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: AbstractGenetic modification using Agrobacterium- or biolistic-mediated gene transfer can overcome the limitations of conventional tree breeding. Alternatives to the use of antibiotic resistance selectable markers and how they might be used to overcome potential public concerns are described. Applications of genetic modifications to trees include altered wood properties, speeding up breeding cycles, forests as pharmaceutical factories, dendroremediation, and improved resistance to pests and diseases, as well as the restoration of sensitive landscapes. Key WordsGenetic modificationtreesforest biotechnologyforestrygeneticsTrees make up more than 30% of the land biosphere and play an essential part in our lives by photosynthesizing, cleaning the air, and contributing to the beauty of landscapes as well as being a major source of fuel and processed products. Progress in improving trees using genetic modification (GM) is discussed herein. Technologies for speeding up tree breeding using biolistics and Agrobacterium-mediated genetic modifications are described. These technologies lead to applications including modifying wood composition and structure, manipulating growth and development, improving pest or disease resistance, and landscape restoration. approaches to tree improvement have involved the identification of mature trees with desirable phenotypes, followed by their incorporation into breeding programs. The length of time needed for trees to reach reproductive maturity, often in excess of 20 years, before controlled crosses can be made is the limiting factor for tree improvement (Martin-Trillo and Martinez-Zapater 2002). Furthermore, there is no guarantee that the desired progeny phenotypes will be identified. The application of biotechnology can overcome many of the drawbacks associated with conventional breeding strategies. This may be achieved using micropropagation or somatic embryogenesis techniques (Dandekar and James 1998) to produce large numbers of phenotypically similar plants, at rates exceeding those attainable in nurseries, or via the use of genetic modification technologies (Trick and Finer 1999) (Table 1). View this table:View inlineView popupTable 1 Genetic manipulation technologies. There is enormous potential for speeding up tree breeding cycles by the use of genetic modification. Systems have been developed for gene transfer, selection of novel gene-containing shoots, and stimulating regeneration for both broadleaved and coniferous trees (Fenning and Gartland 1995; Wenck et al. 1999). The systems use either the soil-based microorganism Agrobacterium or DNA-coated p1. [SEP] Justify:

Output: The text contrasts the claim by discussing the various applications and limitations of genetic modification in trees, without making any assertion that these modifications will entirely end global warming. It mentions advantages such as improved pest resistance and altered wood properties, but does not suggest that genetic modification alone can solve the significant global issue of climate change.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Dolginov, where the scientist got the equation of field generation [7] : ∂ B ∂ t = [ ∇ [ v B ] ] + c 2 4 π [ ∇ ( 1 σ [ ∇ B ] ) ] − [ ∇ [ j B ] e N e ] − c [ ∇ η ∇ T ] (1) (where η is thermoelectrical coefficient). He thought that the forth from right member [ ∇ η ∇ T ] was different from zero and generated the star’s magnetic field, whereas the first three members could be neglected because they were small and could not be performed at the considered model of moving unmagnetized plasm. His calculations showed that the dipole field of a star on its surface can be about 10−4 T. Unfortunately, A. Z. Dolginov never presented a detailed theory of making magnetic fields of space objects with thermoelectrical currents, which are immanent to “hot” objects. Below, there is the third attempt to reanimate geothermoelectrical currents below. It is shown on the example of Earth that those geothermocurrents, moving directionally inside the conducting shells (the core and the lower mantle) and affected by the great difference of geotemperatures, can generate not raw but real powerful magnetic fields of Earth and other “hot” planets of the solar system, because the phenomenon is universal. That attempt is based on the results of geophysical field work for many years, experimental and academic research, which let the author elaborate a new model of thermo-electrochemical nature of the Earth’s core natural electric field [13] [14]. It is shown that the potential-forming factor of a natural electric field is not the reduction-oxidation zone, but a constantly functioning extraneous source of non-electric origin, which is geotemperature gradient ∂T/∂z. Affected by only the latter (∂T/∂z vector is always directed deep into the earth) the anomalies of the natural electric potential, registered at all the Earth’s continents, are steadily negative. [SEP] Justify:

Output: The text discusses scientific information about the generation of magnetic fields related to the Earth's core and thermoelectrical currents, but it does not mention sea turtles or their navigation methods. Therefore, it is irrelevant to the claim about sea turtles navigating using the Earth's magnetic field.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: AI enabled voice-assistants are developed based on the personalities of machine learning models combined with natural language programming (García-Méndez et al. 2021). AI-based voice-assistants uses speech recognition which are systems that have the ability to collect and interpret spoken information by converting the inputted voice command to text. Existing speech recognition systems such as Microsoft’s WisperID that recognize what is being said but also recognizes the particular speaker ( in situation where different vowel sounds or accents from different sound origin are analyzed to recognize the speaker (Wedberg and Dalgren 2021). Indeed, AI-based voice-assistants is being adopted as a potential aid for older people due to early-stage adoption of such applications for health management, though these systems are in use due to improved readiness level many barriers still remain that impacts their successful adoption by the aging population (Gudala et al. 2022). It has been adopted in different areas such as in e-commerce, healthcare, weather reporting, reading news, play music, etc. by older people. AI-based voice-assistants use a microphone embedded in a device to receive commands from the user’s voice, and it converts the inputted voice into a text sentence (utilizing a natural language parser), and it executes the suitable command stated within the sentence. As output, AI-based voice-assistants uses a speaker embedded in a physical device to communicate response to individuals as the outcome of the command based on ML models (Capodieci et al. 2018). These ML models possess the capability to understand spoken human language learn and adapt from the interactions, thereby increasing the knowledgebase (2021), enabling the system to recall tasks assigned and personalization, connect remotely with family members and caregivers. AI-based voice-assistants are useful to improve the welfare and well-being of older people by increasing their quality of life. AI-based voice-assistant are intelligent software that can learn from prior experience using ML algorithms and NLP (Tascini 2019). Due to the advancement of AI enabled voice-assistants to intelligently generate responses to end user requests it is now being adopted by older people in different domains such as healthcare, weather, news, etc. (de Arriba-Pérez et al. 2021; Gudala et al. 2022; Miura et al. 2022). [SEP] Justify:

Output: The text corroborates the claim by describing how AI-based voice assistants use machine learning (ML) models and natural language processing (NLP) to learn from prior interactions, adapt to user preferences, and provide personalized responses. The text specifically mentions that these systems have the capability to understand spoken human language, learn, and adapt, which aligns with the claim that AI assistants are able to learn user preferences to improve recommendations.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: Hairy roots of willow (Salix spp. hybrid). P62. 5 million) of research by more than 400 multinational research consortia, that GM crops are “probably … safer than conventional plants and foods” for both the environment and human consumption (Bouchie 2001). Additionally, coordinated studies from U. S. and Canadian scientists have shown that the impact of Bacillus thuringiensis (Bt) corn pollen from current commercial hybrids on monarch butterflies is negligible (Sears et al. 2001). Antibiotic resistance markers have been extensively used in food crop trials for at least 15 years (Stewart and Wheaton 2001). Alternative selectable markers being developed include the isopentenyl transferase gene from the plasmid embedded within a transposable element (Ebinuma et al. 1997). An estradiol-induced recombinase protein can excise DNA at specific recognition sites flanking a selectable marker gene (Ow 2001). Genes for detoxifying unusual substrates such as 2-deoxy-glucose-6-phosphate, or genes enabling unusual nutrients to be assimilated, such as phosphomannose isomerase (Hansen and Wright 1999), have also become available. Recent EU legislation limits the use of antibiotic resistance marker genes where there is evidence of significant potential damage to human health or the environment. Similarly, the potential risks from individual herbicide resistance genes being transferred to other plants must be put into context. Resistance to herbicides, such as Round-Up® or glyphosate, the most commonly quoted in anti-GM literature, can only become a significant problem if we rely on it as a sole source of killing weeds (Strauss et al. 1997). More important, it is often overlooked that use of herbicides in forestry is infrequent due to health concerns and there are distinct advantages in using non-persistent formulations with low ecotoxicity. It is likely that new and future generations of herbicides, with different modes of action, will be transferred from the annual crops arena to forest trees. This is likely to greatly reduce our reliance on glyphosate, the active ingredient in Round-Up®, as other herbicides become commonplace. Herbicide resistance genes may also provide new alternatives to the use of antibiotic resistance in future GM trees. The potential for trees to spread pollen over long distances is not disputed. [SEP] Justify:

Output: The text contrasts the claim by discussing various factors associated with genetically modified (GM) crops and trees, focusing on their safety, herbicide resistance, and environmental impact without establishing any claim that GM trees will end global warming. It emphasizes limitations and potential risks, indicating that while GM technologies may present advantages, they do not imply that GM trees will solve global warming.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Besides, only the geotemperature gradient forms two poles along the fall of natural electrical conductors: a negatively charged inner part of conductors and a positively charged outer one (positive pole) in the upper part of the poles, as well as positively charged inner part of conductors and the negatively charged outer one (negative pole) in the lower part of the poles. Thermoelectrical currents move inside conductors if the temperature difference applied to the ends of ore bodies is in dynamic state. If the state is static there is no thermoelecrical current, but there is still difference of potentials between the cold and the warmed ends of the conductor. Hereinafter all the calculations of a planet’s magnetic field are based on the known physical laws and known scientific data of inner structure and physical properties of Earth. The results show that the actual Earth’s thermoelectrical currents generate and maintain the current geomagnetic field by themselves. Besides, it is the first time when a model specifies complexity of the geomagnetic field, which consists of two parts: main magnetic field of the metal core and additional field of the lower mantle. Accordingly, the schematic geothermoelectrical model (protomodel) of the Earth’s magnetic field is considered for the first time in the author’s 2. Calculations of for the To form the geomagnetic model one should procced from the known scientific data of the planet’s shells thicknesses, temperature values in the shells and their borders. For example, most of the researchers hold on to the following defining temperatures in the body of our planet: on the depth of about 100 km the temperature is close +1800 K, on the depth of about 400 km (C layer, phase transition zone) the temperature is +1900 K, on the “mantle-core” border the temperature is +4000, …, +5000 K, in the core center the temperature is (+5000, …, +6230) ± 500 K [16] - [21]. [SEP] Justify:

Output: The text does not address the claim about sea turtles navigating using the Earth's magnetic field. Instead, it delves into the complexities of the Earth's magnetic field, thermoelectrical currents, and temperature gradients within the planet's layers, which are unrelated to how sea turtles use the magnetic field for navigation.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: to easily change the factory settings or configurations based on the mobility and walkability needs of older people (for example enable/disable the auditory interface connected to a specific feature in physical devices such as smartphones). In addition, the visual output should be user-adaptable or self-adaptive to provide best interface match to older people ability and desire. Personalized mobility and walkability suggestions should be based on older people requirements and needs, and it might be beneficial if the AI-based voice-assistants is designed to provide personalized pedestrian advice on-demand recommendation using data from different sources such as crowdsourcing data, open data, historical/batch data, real-time data, etc. (Chen et al. 2021). Furthermore, the auditory interface should be designed in a way that it can improve older people awareness of adaptations thereby providing a medium to preview, check, discard, override, revert, save, recover, and test any possible adaptations (Gonçalves et al. 2021). Older people should be provided with easy, explicit, and clear guidance during setting up and future troubleshooting of the AI-based voice-assistants installed in the device (Chen et al. 2021). To enhance the service design of AI-based voice-assistants suitable for older people, designers should address low system performance by changing the functionalities provided by the designed systems, not changing how users utilize the designed system (Chen et al. 2021). Research suggested that voice-assistants should be multimodal, precisely incorporating non-verbal communications. Multi-modality is an important design requirement to be considered in the development of auditory interface for systems (Abdolrahmani et al. 2021). The voice-assistants should have easy access that provides help to users (Wedberg and Dalgren 2021), from a design perspective, for first time users the application should have some sort of introduction or short user guide, that is intuitive and user friendly to understand. Nonetheless, it is crucial to mention that these requirements will vary depending on the older user’s technological knowledge, difficulties, and background (Wedberg and Dalgren 2021). Since older people would prefer conversing with a real person rather than interacting with a computer-generated voice in case of an emergency. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI-based voice assistants can be designed to provide personalized recommendations tailored to older people's mobility and walkability needs. It emphasizes the importance of user adaptability and a personalized approach using various data sources, indicating that AI can learn user preferences to enhance service delivery.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: Where GM trees are concerned, scientists cannot yet completely guarantee that pollen formation can be prevented in every individual tree, although exercising flowering control has been suggested both for limiting invasiveness as well as potential for pollen transfer (Adams et al. 2002). Such an expectation, however, misses an important question: What impact would GM pollen grains have if released into the environment? There is currently no clearly compelling evidence of significant damage due to limited amounts of GM tree pollen being able to spread within the environment (Strauss et al. 2001). Plants, including trees, have been exchanging genetic material within and between species on a large scale for millions of years. As yet, this exchange does not appear to have produced large-scale ecological problems. If transfer of GM tree pollen really is a significant issue, then sterility systems for inducing reproductive incompetence (Mouradov and Teasdale 1999) or trunk grafting (Lev-Yadun and Sederoff 2001) will become more useful in alleviating these concerns and their use encouraged in GM trees (Strauss et al. 1995). Even if a distinct adverse effect of allowing GM trees to flower is found, beyond that of simply promoting genetic exchange, it is likely that any such effects could be ameliorated using short rotation forestry, with limited, or indeed no flowering potential. Once again, any risks introduced from limited GM tree pollen transfer must be placed into context versus the risks from introduced exotic germplasm (Richardson 1998). Additionally, recent evidence suggests that transgenic poplar DNA is unlikely to persist in soils for more than 4 months before being degraded, limiting potential for transfer to soilbased microbes (Hay et al. 2002)Two other potential concerns expressed about GM trees relate to their longevity and the potential for unexpected effects, once released into the environment. Unexpected effects might include gene silencing, in which expression of an inserted gene ceases. Releasing and monitoring a large number of independently genetically modified trees could overcome this potential problem, because the available evidence suggests the frequency of gene silencing events is rare (Dominguez et al. 2002a). A similar approach is already taken by commercial foresters, who generally wish to avoid monocultures. Additionally, our understanding of genetic stability is increasing as sequences such as matrix attachment regions (MARs), which appear to improve the consistency of gene expression, become more widely used (Allen et al. 2000). [SEP] Justify:

Output: The text contrasts the claim by addressing uncertainties and concerns surrounding genetically modified (GM) trees. It discusses the lack of definitive guarantees regarding the environmental impact of GM tree pollen and suggests that potential risks must be contextualized against other ecological concerns. The discussion implies that while GM trees may have benefits, there are significant unknowns, indicating that they cannot alone solve global warming.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: As for the planet’s electroconductivity, the researchers believe it is distributed in depth (shells) as follows: the upper mantle has low electroconductivity and, predominantly, ionic conduction; in the C layer, semiconducting electroconductivity increases after transiting from the ionic conductivity; the lower’s mantle material is an electronic semiconductor; the Earth’s core is an electronic conductor with the specific conductance in the range of (0. 1 ÷ 1. 0) × 106 S・m−1 [16] [17] [21] [22]. That data implies that the globe can be presented as a spherical geothermoelecrical element (further, SGTE) (Figure 1(a)), which ends are the central part of the core and the C shell (most likely, its middle). The temperature difference of about +3200 K, …, +4500 K is attached to those ends. Then, according to the Seebeck effect, the thermo-EMF ΔφТ can be calculated [23] [24] : Δ φ T = ∫ T 1 T 2 β d T (2) where β is the averaged temperature coefficient, which is 0. 0001 V・deg−1. for pure metals, while it is 0. 0015 V・deg−1. for some semiconductors [24]. To find the parameters of the lower mantle thermocurrents Itop the following must be known: - thermofield intensity ET; - electron concentration per unit volume of the lower mantle ores (it equals n = Figure 1. Scheme of the electron flows movement within the spherical metal core and the spherical circle of the lower mantle: (a) projection of traces of oppositely moving electrons of the metal core (to the core’s center) and the lower mantle (to the Earth’s surface) on the equatorial plane; (b) projection of traces of unidirectionally moving electrons of the metal core and the lower mantle to the Earth’s surface on the equatorial plane; (с) dipolar poloidal magnetic field of the Earth. 1: electron; 2: electron trajectories; 3: the lines of force of toroidal field; 4: the lines of force of self-generated poloidal field. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the electroconductivity of the Earth’s layers and thermoelectric effects without mentioning sea turtles, their navigation, or the Earth’s magnetic field. There is no information related to the claim within the provided text.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: Also, the affordability and cost of acquiring devices where AI-based voice-assistants can be implemented could be challenging for older people from lower socioeconomic. Conversely, this depends on the technological readiness of the age group of older people as those between 65–74, 75 and older may face technical difficulty whereas those between the age of 55–64 may have some experience with technology and thus requires a much more attractive and elaborate solutions (Chattaraman et al. 2019). Accordingly, the technological experience of the older users must be considered when providing AI-based voice-assistants that offers personalized safe mobility and walkability support. As the degree of personalizing behavior for reducing risk of injuries, falls and accidents may lead to increased uptake and acceptance of the technology (da Paixão Pinto et al. 2021). Hence, designing an AI-based voice-assistants suitable for the aging population in the society is a challenge (Chattaraman et al. 2019). As anticipated, the easier the interaction mode, the greater the acceptance and use of the voice-assistant system. Thus, AI-based voice-assistants that stimulate physical activities such as walking and cognitive skills such as wayfinding is recommended in the literature. As such AI-based voice-assistants can be customized, via adaptation of information, suggestions, and feedback based on older users’ preference (da Paixão Pinto et al. 2021). Evidence from the literature argued that issues associated with remembering certain keywords for using some features in the AI-based voice-assistants was a challenge for many older participants, stressing concerns about the cognitive accessibility of this digital technology. In comparison to conventional devices, this challenge might not occur as often, since the input feature is visible to older user (Pradhan et al. 2020). In addition, instead of assuming that older users remember the accurate keywords, designers of voice-assistants should employ heuristics of checking older user input, i. e. , if individuals forget to input “exact keywords”, the voice-assistant should predict possible input request and confirm it from the individuals. Likewise, contextual cues (such as the current and past location of the user, recently inputted requests from the user, most visited location, etc. ), can be incorporated into the dialogue design where possible (Pradhan et al. 2020). [SEP] Justify:

Output: The text corroborates the claim by discussing how AI-based voice-assistants can be customized to adapt information, suggestions, and feedback based on older users’ preferences, as mentioned in the phrase 'AI-based voice-assistants can be customized, via adaptation of information, suggestions, and feedback based on older users’ preference (da Paixão Pinto et al. 2021).' This indicates that AI assistants have the ability to learn and consider user preferences to enhance their recommendations and support.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: The relative longevity of trees is as much a benefit as a potential concern, because their potential economic or amenity value can extend for hundreds of years. Many of the publicized potential concerns about GM trees are either misplaced due to a lack of public understanding, or they may be actively managed until such times as further research, including field trials, can resolve these issues. It should not be forgotten that, since 1994, more than 3. 5 trillion GM plants have been grown in the alone, without any significant ecological or human health consequences being identified (Stewart and Wheaton 2001). These numbers include more than 250 field trials of GM trees, although most of these have been with poplars and eucalypts (Rautner 2001; Gartland et al. 2003). Our knowledge and understanding of how GM trees function and perform in the wider environment can only be aided by further field trials, using a wider range of trees, as they become available through advances in genetic modification and regeneration technologies. of the potential benefits that genetic modification can bring to forestry and arboriculture include improved yields; modifying lignin content; speeding up breeding cycles; the production of valuable pharmaceuticals from trees; cleaning up environmental pollution; more effective management of pests and diseases; and enhanced amenity and landscape restoration value. Increasing yields from plantation forests by genetic modification will allow more wild forests to be left undisturbed. To this end, findings that at least two species of aspen (Populus tremuloides and P. tremula) grow faster after genetic modifications are most encouraging (Hu et al. 1999; Tzfira et al. 1999) (Figure 2). Among the wood quality traits that can now be altered by genetic modification are the composition and processing properties of lignins. Using antisense technology to reduce expression of 4-coumarate:coenzyme A ligase, a key step in lignin biosynthesis, reductions of up to 45% in the lignin content and increases of up to 15% in the cellulose content have been obtained in aspen. Enhanced leaf, root, and stem growth were also observed (Hu et al. 1999; Sederoff 1999). Being able to reduce the lignin content of trees or to improve pulping properties has also been achieved by reducing cinnamyl alcohol dehydrogenase activity in 2-year-old poplar (P. tremula × P. [SEP] Justify:

Output: The text contrasts the claim by indicating that while genetically modified trees may provide various benefits, such as improved growth rates and environmental remediation, it does not support the assertion that they will end global warming entirely. It highlights the importance of further research and field trials to understand their ecological impact, but emphasizes potential concerns, benefits, and limitations rather than a definitive solution to global warming.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: 1020 m−3 by default); - electron gas drift velocity v d - electron mean free path in a semiconductor l; - current density j. According to the (2), the maximum thermo-EMF ΔφТ for SGTE is 0. 45 V, and the electric field intensity can be found from the following equality: E T = Δ φ T l SGTE V m (3) where l SGTE is SGTE radius length, which equals the distance between the planet center and the middle part of the C shell and is defined as 5438 km or 5650 km to the upper mantle. Hence, the electric field ЕТ generated by the existing temperature difference ΔТ makes planetary flows of “hot” electrons move directionally from the planet center to its outer shells along the radii. At the same time the electron quantity (concentration) in a volume unit of metal (Fe) can be calculated with the formula [24] : n = ρ N A A m − 3 (4) where ρ is the metal Fe density in the inner core, (12. 2 g・cm−3); NA is the Avogadro constant, 6. 02 × 1023 mol−1; A is atomic mass Fe (55. 847 g). Substituting that data into the (4) we get n = 1. 32 × 10 29 m − 3. n should be compared to the calculation for the real conditions when electron gas is in free state at pressures p, that corresponds to the metal core, i. e. at p = 3. 55 million atm [25] [26] [27] : n = ( 5 m е ℏ 2 ( 3 π 2 ) − 2 3 ) 3 5 p 3 5 (5) Knowing that electron mass m e = 9. 11 × 10 − 31 kg and p = 3. 59 × 10 11 Pa a new magnitude can be found n = 3. 25 × 10 29 m − 3. Despite there is a slight discrepancy between magnitudes ni (just 2. 5 times), the magnitude n = 3. 25 × 10 29 m − 3 is used for the further calculations, as it meets the real planet conditions most. [SEP] Justify:

Output: The text is irrelevant to the claim regarding sea turtles and their navigation using the Earth's magnetic field. Instead, it discusses concepts related to electron gas, electric fields, and planetary conditions, without any mention of sea turtles or their navigation methods.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: (2021) advocated for individuals to choose from different modalities such as vibrations, earcons, or spoken output to receive notifications/responses offered by the voice-assistants (for example when users are heading toward a wrong route, walking pass a favorite Point of Interest (PoI), accessing a ride-sharing service). Overall, the integration of voice interactions with additional services and modalities in a meaningful way to facilitate diverse needs of older people in urban contexts will improve the usability of AI-based voice-assistants while providing a more natural means of communication (Abdolrahmani et al. 2021). Furthermore, to improve wayfinding to older people when they go for walk the contents of the voice-assistants must provide clear, easy, and understandable information, which is compatible across different device screen. When providing basic information, it is important to provide older people with the possibility of modifying the information based on the user’s accessibility preference and user requirements. Image information can be included to be supplemented by description of the information, which is to be understandable, irrespective of the user’s knowledge, experience, or current level of concentration (Peraković et al. 2015). It is encouraged to disable procedures that create information that require the full concentration of the user. The operation and use of the voice-assistants should not require much mental or physical effort from the user. The AI-based voice-assistants can provide real-time traveler information for older people when they use public transportation for their mobility needs. The real-time service can inform the user using voice or audio information regarding any changes based on their pre-planned route when they go for walking. For instance, if there is construction or road closure along the pedestrian crossing and if there is a chance of not being able to cross, the individual receives information on this, and the voice-assistant provides suggestions about different routes for safe walking (Peraković et al. 2015). To improve safe mobility and walkability for older people the voice-assistant needs to be adaptable scalable, and flexible. To achieve this previously validated machine learning models used in similar contexts for other people in the past can be adjusted to the current needs of older people. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI-based voice assistants can adapt their interactions based on users' preferences and needs. It mentions that the voice assistants provide information in a way that is 'compatible across different device screens' and allows users, particularly older individuals, to modify the information based on their own accessibility preferences and requirements. Additionally, the mention of using 'validated machine learning models' indicates that AI systems can learn and tailor their recommendations to fit specific user needs.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: alba) (Lapierre et al. 1999). This process could reduce the energy inputs required and pollutants released by the pulping industry. Further enhancements of vegetative growth and rate of biomass increase may become possible by manipulating ammonium metabolism in trees. Increased cellular glutamine synthetase activity can enhance synthesis of nitrogenous compounds and biomass growth (Suarez et al. 2002). The understanding of how wood is laid down under stress conditions is being enhanced by studies on xylem tension stress-responsive genes (Samuga and Joshi 2002) and the effects of rol C gene expression in developing xylem in aspen (Grunwald et al. 2002). Download figureOpen in new tabDownload powerpointFigure 2. Genetically modified Populus species hybrid. Some of the time-related limitations of conventional tree breeding may be overcome by expressing thale cress (Arabidopsis thaliana) genes, such as apetela 1, or leafy in trees. Already, apetela 1 expression has shortened generation times from seed to seed from at least 5 years to a single year in the commercial citrus production rootstock citrange (Citrus sinensis × Poncirus trifoliata) (Pena et al. 2001). This result was achieved by converting apical and lateral shoots to flowers, and it is likely to have significant economic implications for the fruit tree industry. Similar results have been demonstrated in aspen, reducing flowering time to a mere 7 months (Nilsson and Weigel 1997; Martin-Trillo and Martinez-Zapater 2002). Although greater understanding of flowering control is still required, the combination of genetic modification and DNA sequence information gained from analysis of earlyflowering mutants from Arabidopsis seems very likely to speed up future breeding programs considerably (Egea-Cortines and Weiss 2001). GM trees also have potential to produce valuable pharmaceuticals. Although at an early stage, rubber trees in Malaysia have been engineered to secrete human serum albumin. Other high-value commodities produced and easily harvested from GM trees, including vaccines in fruit, will no doubt become commercial realities in due course (Langridge 2000). GM trees have significant potential for dendroremediation—using trees to clean up environmental pollutants. Restoring contaminated land sites reduces risks to the environment and greatly enhances the value of these sites. Considerable progress has been made with both heavy metals and with organic pollutants. [SEP] Justify:

Output: The text contrasts the claim by discussing various benefits and advancements of genetically modified (GM) trees, such as improving agricultural efficiency, producing pharmaceuticals, and remediating contaminated land. However, it does not support the assertion that GM trees will entirely end global warming, instead highlighting their potential in specific applications without making definitive claims about their ability to resolve global warming.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: In its turn, the total sum of electron gas in the metal core is defined as m Σ = 4 3 π R c 3 m е n = 5. 09 × 10 19 kg. The same must be performed for the lower mantle, spherical shell circle thickness is Δ R l m = 2. 22 × 10 6 m n = 1 × 10 20 m − 3 (average value for semiconductors from 5 × 1019 m−3 to 5 × 1020 m−3 [28] ), the electron mass m e is the same. Then m Σ l m = 4. 35 × 10 9 kg. To calculate the electron gas drift velocity v d according to the quantum theory of the electrical conductivity of metals, the following formula should be used [24] [26] [27] [29] [30] : v d = e E T l 2 m v m ⋅ s − 1 (6) where e is the effective electron charge, C (1. 6 × 10−19); m is the effective electron mass, kg (0. 91 × 10−30); l is the electron mean free path, it equals l = σ ℏ ( 3 π 2 ) 1 3 e 2 n 2 3 m (7) where σ is the planet’s core electroconductivity, S/m; ħ is the Plank’s constant, 1. 05 × 10−34, J・s; v is the average velocity of electrons thermal movement, it can be derived from the formula: v = 8 k T π m m ⋅ s − 1 (8) where k is the Boltzmann constant, 1. 38 × 10−23 J/K; As seen, it is necessary to calculate the velocity of the electron thermal movement at T = 6000 K preliminary, according to (8), to calculate ЕТ and the electron mean free path l between their concussions with the crystal cell to find v d. l directly depends on the metal core’s electroconductivity which can be taken as 105 S/m as the smallest magnitude from the above interval (0. 1 ÷ 1. 0) × 106 S・m−1. Substituting the corresponding values into (8), (7) and (6) one will get v = 4. [SEP] Justify:

Output: The text is irrelevant to the claim regarding sea turtles navigating using the Earth's magnetic field. It discusses the properties of electron gas in the metal core and quantum theory of electrical conductivity without mentioning sea turtles or navigation at all.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: The employed machine learning models should also be instantiated and adapted to reflect new features and interactions evolving from the use of voice-assistant in handheld devices for mobility and walkability purposes (Sochor 2015). Older people should evaluate and practice with the AI-based voice-assistants in a real environments and contexts, thus ensuring safe mobility and walkability. The user interfaces and auditory interface should be intuitive to support the best possible interaction mode for older people. To promote inclusive design older people with visual disabilities, the interfaces can be designed in ways that aids information to be perceive. When the GUI includes text (in labels and menus), it is recommended to use sans serif fonts, as the resolution of the device screens are typically lower than paper. This makes reading from the screen less exhausting and easier for older people. A contrasting color (such as black/white or blue/yellow combinations), and clear interlining spacing can also be considered. Iconic photographic pictures with good contrast with the background color should be used (Sochor 2015). Audio prompts can also be included so older people can build a mental model of the digital environment, thus improving their interaction with the voice-assistants. To improve interaction with the voice-assistant diverse ways of navigating the system should be provided. The voice-assistant should use excellent quality and easily understandable audio. Audio clues such as non-spoken sounds can be used as they are easier to listen to as compared to verbal audio cues, since they do not distract older pedestrians during navigation. Iconic sounds occasionally referred to as earcons are applicable to convey navigation related information, whether it is specific, general, or quantitative (Sochor 2015). Mobility related information should always be available to older people, and they should be able to listen to important messages. The design of the voice-assistants should support asynchronous feature when the system required to provide auditory information without being spoken back to and the system should be able to repeat important messages. Navigational recommendations provided for safe walkability should be simple and clear contextualized information since older people may be distracted as they walk within the build environment. The AI-based voice-assistants should provide contextualized information about the orientation and position of users, their destinations, landmarks, and obstacles to avoid. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI-based voice-assistants can improve interactions by adapting to user needs, such as providing contextualized information about orientation, destinations, and obstacles. This indicates that the AI assistants are indeed learning from user interactions and preferences to enhance their recommendations and support user experience, particularly for older adults. Such personalized adaptations reflect the overall capability of AI assistants to learn and tailor their functionalities to improve user recommendations.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: Yellow poplar (Liriodendron tulipifera) modified to express bacterial mercuric reductase grows vigorously in normally toxic levels of ionic mercury, being able to convert the highly toxic ionic mercury (Hg [II]) to the much less toxic elemental form (Hg [0]) up to twelvefold faster than untransformed poplars (Rugh et al. 1998; Meagher 2000; Rugh 2001). Using cultures of genetically modified poplar cells, Gordon et al. (1998) were able to degrade trichlorethylene, one of the world’s major pollutants. Field study data have shown that up to 95% of available trichlorethylene was degraded to chlorine ions and CO2. Similar approaches are being followed with a range of other organic pollutants, including the explosives RDX and 2,4,6-trinitrotoluene (TNT) (Thompson et al. 1999). Using trees to clean up environmental pollutants, contaminated sites can be returned to useful service for housing, leisure, or light industry. Arboricultural benefits from genetic modification are also likely to come from improved resistance to pests and diseases. Examples include introduced resistance to the fungal pathogen Septoria musiva in poplar expressing chitinbinding proteins Ac-AMP1. 2 and ESF12 (Liang et al. 2002) and plum pox virus in Prunus domestica following expression of the viral coat protein (Ravelonandro et al. 1999). In Polish, Spanish, and Rumanian field tests, the GM plum trees allowed only low levels of viral multiplication from which the trees rapidly recovered. One such line was not infected at all over the 3-year duration of these trials, in contrast to the untransformed trees that all became infected during the first year (Ravelonandro et al. 2000). Resistance to the cottonwood leaf beetle (Chrysomela scripta), a major pest, may soon be possible among poplars following their genetic modification with Bt toxin genes (James et al. 1999). Similar trials are under way in China using the Cry 1 Ac gene in P. nigra to protect against the defoliators (Hu et al. 2001). Particular care is, however, likely to be needed when selecting appropriate Bt toxin strain genes due to regional differences in cottonwood leaf beetle susceptibility. [SEP] Justify:

Output: The text contrasts the claim by highlighting that genetically modified trees have specific uses, such as degrading pollutants rather than directly addressing global warming. The discussion on the modified yellow poplar's capability to convert toxic mercury and degrade trichlorethylene does not support the idea that they will eliminate global warming entirely. Additionally, while genetic modifications provide resistance to pests and enhance environmental clean-up efforts, they do not guarantee the complete resolution of global warming.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Then the total value of the lower mantle thermocurrents Itop, coming through its outer spherical surface SD with the radius RD can be derived from the following formula: I t o p = j S D (11) And, finally, according to the Biot-Savart-Laplace law, the Earth’s magnetic induction B Т Т t o p on the bottom surface equator can be calculated: d B Т Т t o p = μ 0 I t o p d R D sin 90 ∘ 4 π R Earth 2 (12) However, the magnetic moment of the current element I t o p d R D is considered here only in a one-dimensional space towards the current movement along the radius R D. Integration over d R D allows to find the total of the current elements I t o p R D just along the way R D. This won’t go for our case, when the radial currents, like Ilow, move from the source (sphere’s center) to the interface with the lower mantle spherical surface, directed azimuthally from 0 to 4π in a spherical conductor. There is no decision on valuation magnetic induction when currents move radially in a spherical conductor in technical literature. That’s why it is better to consider the following model. A small spherical conductor with the radius d R i is put into the spherical conductor with the radius R Σ. The currents, moving from the spheres center make a magnetic field in the volume of the initial small sphere. That field is always directed to its volumetric surface tangentially. Then, according to the circulation theorem, d B ⋅ 4 3 πd R i 3 = μ 0 μ r I i ⋅ 4 πd R i 2 where the currents I i coming through the small sphere surface 4 πd R i 2. When the current is distributed evenly over the sphere’s section I i = I l o w ( d R i R Σ ) 2. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim about sea turtles and their navigation using the Earth's magnetic field. The content discusses the calculation of magnetic induction and currents in a spherical conductor without any reference to sea turtles, their navigation, or the use of the Earth's magnetic field.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: The system should be able to adapt to user’s environment by adequately providing hints that fits the mobility and walkability needs and experience of the current older user (Sochor 2015). However, the customization should be executed by the user by adapting the voice-assistant and providing the system with information which is used by the machine learning to learn and plan and recommend present and future mobility and walkability personalized to the user needs (Sochor 2015). The device in which the AI-based voice-assistants is installed and deployed to be used by older people should be portable so that it can be comfortably carried in their hand or in their pocket. Overall, when older people use voice-assistants for route navigation it should not affect their primary walking aid utilized such as walking canes, rollators walking aid users, and wheelchairs, electric mobility scooter, etc. AI-based voice-assistants should consume less battery, such that older people can use it to provide uninterrupted mobility services for long distant travel. AI-based voice-assistants should enable older people to carryout straightforward service-booking, provide information to older people related to walking times to and from bus stops, trip time, frequency of public transport service (e. g. , city bus), changing or connection between different forms of transport and availability of shelters at bus stations/stops. Information related to availability of seat on public transport service such as buses for older people (Levin et al. 2012). If possible in designing AI-based voice-assistants there is need to reuse existing devices and infrastructure that provides information needed for safe navigation. This implies that older people should be able to autonomously navigate urban environment e. g. , taking the stairs or through narrow hallways (Sochor 2015). 4. 2. 5 Designed architectural modelTo address RQ3: what AI techniques are suitable to design an architectural model for a voice-assistants that provide information to older people for safe mobility and walkability? This section presents the designed architectural model for the voice-assistants as seen in Fig. 9 to be used to implement the AI-based voice-assistants. The designed architectural model captures the AI techniques (machine learning, NLP, and speech recognition), needed to achieve a user-centric experience that improves mobility and walkability safety toward reducing falls, injuries, and accidents by older people. [SEP] Justify:

Output: The text corroborates the claim by emphasizing that AI-based voice assistants can adapt to the user's environment and preferences through customization. It states that users need to provide information to the system, which is utilized by machine learning to learn and improve personalized recommendations for mobility and walkability, thereby supporting the idea that AI assistants are learning user preferences to enhance their recommendations.


Input: Task: contrast the claim using the given text. Claim: Genetically modified trees will end global warming entirely. [SEP] Text: White pine blister rust (Cronartium ribicola) has attacked pines in the (Pena and Seguin 2001). These trees, described by as the priests of pines addressing the surrounding forest, may benefit from a combination of genetic modification and genomics—the application of computer science to genetic structure and function. Gene mapping and genomic sequencing advances are likely to identify potential genes for resistance to white pine blister rust fungus. Such genes, once allied to suitable regulatory elements, could be transferred back into the pines by genetic modification, reducing future damage to the landscape. genetic modification is most likely to be acceptable to the public in two areas: where greater productivity from reduced plantation forest areas can be shown to increase areas left to nature’s own devices, and in restoring threatened trees to damaged landscapes, such as the elm. Whichever aspects of GM trees advance most rapidly in the future, environmental risk assessment should always be carried out, on a case-by-case basis, until a sufficient body of knowledge on the anticipated benefits and the possible risks of this exciting technology is established. © 2003, of Arboriculture. All rights reserved. ↵Adams, J. M. , G. Piovesan, S. Strauss, and S. Brown. 2002. The case for genetic engineering of native and landscape trees against introduced pests and diseases. Conserv. Biol. 16:874–879. OpenUrl↵Allen, G. C. , S. Spiker, and W. F. Thompson. 2000. Use of matrix attachment regions (MARs) to minimize transgene silencing. Plant Mol. Biol. 43:361–376. ↵Bouchie, A. 2001. Safety of GMOs reaffirmed by EU. Nat. Biotechnol 19:1095. OpenUrl↵Dandekar, A. M. , and D. J. James. 1998. High levels of expression of full-length cry IA(c) gene from Bacillus thuringiensis in transgenic somatic walnut embryos. Plant Sci. 131:181–193. OpenUrl↵Daniell, H. , and A. Dingra. 2002. Multigene engineering:Dawn of an exciting new era in biotechnology. Curr. Op. Biotechnol. 13:136–141. ↵Davey, M. R. [SEP] Justify:

Output: The text contrasts the claim by suggesting that while genetic modification can aid in combating specific threats to trees, it does not claim that genetically modified trees will entirely eliminate global warming. It emphasizes the potential for GM trees to improve productivity and restore damaged landscapes but also stresses the need for environmental risk assessments and acknowledges that these trees can't single-handedly resolve global warming.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Hence, after substituting and shortening, here is the Biot-Savart formula for calculating the magnetic field induction inside the sphere (metal core) as the following rationalized form: d B Т Т l o w = 3 μ 0 μ r I l o w d R i 4 π R Σ 2 or B Т Т l o w = 3 μ 0 μ r I l o w R Σ 4 π R Earth 2 sin α (13) where R Earth is the Earth’s radius, R Σ is the metal core radius. In its turn, the magnetic field on the Earth’s surface for the lower mantle spherical layer can be calculated with considering the thickness of that layer ΔRD, which is denoted as d R D in (12). B Т Т t o p = 3 μ 0 μ r I t o p Δ R D 4 π R l m 2 sin α (14) The heat Q, which is produced in the lower mantle Vlm because thermocurrents interact with active resistance of this mantle’s ores. It can be found from the following formula [23] : Q l m = 1 σ j 2 V l m Δ t (15) where j is the current density in the lower mantle, A・m−2; σ is the lower mantle’s electroconductivity Sm−1; Δ t is the time of producing heat for 31,536,000 c (year); V l m is the volume of the lower mantle’s spherical circle. All the calculations for the lower mantle, including the used constants, are tabulated to Table 1. As seen from the table, the value of the magnetic induction B T T t o p on the Earth’s surface is ~4. 62 × 10−6 T, which is much lower than the really measured for the same terrestrial conditions В 0 = 3. 4 × 10 − 5 T. The heat Q, which is produced in the lower mantle volume and determined by thermocurrents Itop, is 2. 84 × 1014 J per year (2. 84 × 1021 erg per year), and this is much lower than the thermal heat loss of the planet per year, which is determined by the value of 3 × 1021 J per year (3 × 1028 erg per year). 3. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the Biot-Savart formula for magnetic field induction and calculations related to the Earth's lower mantle. It does not mention sea turtles, their navigation, or the Earth's magnetic field in relation to sea turtles, thereby providing no support or contrast to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI assistants are increasingly able to learn user preferences to improve recommendations. [SEP] Text: Thus, the architectural model mainly comprises of the three fundamental AI techniques (machine learning, natural language processing, and human–computer interaction components (speech recognition)), needed to deploy a voice-assistant system. The overall architectural components are designed based on prior studies that developed architectures (Tsiourti et al. 2016a; Su et al. 2017; Wiratunga et al. 2020; de Arriba-Pérez et al. 2022). The designed architectural model is presented in Fig. 9, and description of each of the AI techniques are subsequently discussed. Fig. 9Designed architectural model for developing the AI-based voice-assistantsFull size image4. 2. 5. 1 Machine learningIn this study machine learning is included as seen in the architectural model to support older people plan their journey and walking. Machine learning modules enable predictions and personalization based on the available data as seen in Fig. 9, to provide suggestions to reduce falls, injuries, and accidents by older people. Essentially, rules are employed to provide recommendations, which describe a change in state of the built environment such as change in broken sidewalks, curbs without cuts, busy roads with no sidewalks, high elevations, construction closures, and landmarks will be used with publicly available data. Older people will also provide and save personal information relevant to their mobility needs (e. g. , age distribution, endurance level, ability to navigate uneven terrain, use of any existing mobility support, disabilities if any (physical, on wheelchair or with legal blindness/vision impairment, sensory, and learning impairment, etc. ). These data (historical, crowdsourcing, real-time data) can be collected from municipalities, individuals, and devices. Using machine learning models data on the road capabilities, elevation changes, road slope, weather (Map), route nodes, traffic situation, public transit, multimodal transit, accessibility, rest opportunities, and facilities (Darko et al. 2022), is provided for personalized route guidance for older people in their handheld devices for safe mobility and walkability once they input their preferred destination to the voice-assistants. The goal is to provide the voice-assistants with functionality to compute predictions and the consequent rules, using different data to describe the safe mobility and walkability needs for older people. [SEP] Justify:

Output: The text corroborates the claim by describing how machine learning is utilized within AI-based voice-assistant systems to enable predictions and personalization based on user data. It notes that machine learning modules 'enable predictions and personalization based on the available data' which directly supports the idea that AI assistants can learn user preferences to enhance their recommendations.


Input: Task: contrast the claim using the given text. Claim: All genetically modified trees are guaranteed safe for every ecosystem. [SEP] Text: AbstractGenetic modification using Agrobacterium- or biolistic-mediated gene transfer can overcome the limitations of conventional tree breeding. Alternatives to the use of antibiotic resistance selectable markers and how they might be used to overcome potential public concerns are described. Applications of genetic modifications to trees include altered wood properties, speeding up breeding cycles, forests as pharmaceutical factories, dendroremediation, and improved resistance to pests and diseases, as well as the restoration of sensitive landscapes. Key WordsGenetic modificationtreesforest biotechnologyforestrygeneticsTrees make up more than 30% of the land biosphere and play an essential part in our lives by photosynthesizing, cleaning the air, and contributing to the beauty of landscapes as well as being a major source of fuel and processed products. Progress in improving trees using genetic modification (GM) is discussed herein. Technologies for speeding up tree breeding using biolistics and Agrobacterium-mediated genetic modifications are described. These technologies lead to applications including modifying wood composition and structure, manipulating growth and development, improving pest or disease resistance, and landscape restoration. approaches to tree improvement have involved the identification of mature trees with desirable phenotypes, followed by their incorporation into breeding programs. The length of time needed for trees to reach reproductive maturity, often in excess of 20 years, before controlled crosses can be made is the limiting factor for tree improvement (Martin-Trillo and Martinez-Zapater 2002). Furthermore, there is no guarantee that the desired progeny phenotypes will be identified. The application of biotechnology can overcome many of the drawbacks associated with conventional breeding strategies. This may be achieved using micropropagation or somatic embryogenesis techniques (Dandekar and James 1998) to produce large numbers of phenotypically similar plants, at rates exceeding those attainable in nurseries, or via the use of genetic modification technologies (Trick and Finer 1999) (Table 1). View this table:View inlineView popupTable 1 Genetic manipulation technologies. There is enormous potential for speeding up tree breeding cycles by the use of genetic modification. Systems have been developed for gene transfer, selection of novel gene-containing shoots, and stimulating regeneration for both broadleaved and coniferous trees (Fenning and Gartland 1995; Wenck et al. 1999). The systems use either the soil-based microorganism Agrobacterium or DNA-coated p1. [SEP] Justify:

Output: The text contrasts the claim by indicating that there is no guarantee that the desired progeny phenotypes will be identified, which implies that genetically modified trees may not be universally safe for all ecosystems. It discusses potential benefits and applications of genetic modification but does not assert that these modifications are without risk.


Input: Task: pass the claim using the given text. Claim: Sea turtles navigate using the Earth’s magnetic field. [SEP] Text: Calculations for the of the calculations were performed for the thermocurrents Ilow moving under the influence of thermoelectrical field ЕТ inside the metal core according to (3) too. To calculate its value two main parameters were taken into consideration: 1) the metal core radius, which is 3500 km (sum of thicknesses of the inner core, of the transitive F zone and the outer core) and 2) the discussed below difference of temperature ΔТ between the center of the planet and the border “core-lower mantle”. According to the literary sources the magnitude ΔТ is for the metal core is quite uncertain and fluctuates within extreme values from 0 K to 1957 K ± 227 K. At the same time, it is known from the same sources that such factors as pressure, electroconductivity, thermal conductivity in the metal core are particularly high, and the electron concentration doesn’t practically depend on tem- Table 1. Calculation of electrical and magnetic parameters of Earth’s lower mantle. perature [33]. It is also known that the velocity of seismic P-waves lengthwise the inner core radius doesn’t practically change and is about 11. 2 km・s, though the pressure changes from 320 to 360 GPa [18]. On the assumption of the said, temperature difference of 2000 K inside the metal core body is unlikely. Considering this and taking the core cooling close to 50 K × (109)−1 years [34] there are grounds to take the real value ΔТ for the metal core as 10 K for the first approximation. This amplitude of the temperature difference between coldnesses (ice ages) and warmings on the Earth’s surface is also in the works [35] [36]. That temperature difference is specified in the V. M. Kotlyakov’s work as “range of the isotopic curve oscillations from glacial periods to interglacial ones equals 10˚C in temperature” [37]. But there is a work where the temperature difference is defined as 14 K on the time period from “65 million years ago till today” [38]. Now let us calculate electric, magnetic and thermal parameters of the solid core, which has temperature dynamics. To do this, let us use the mentioned formulas (2)-(11) (13) (15). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses calculations related to the Earth's metal core, temperature differences, and seismic wave behavior without addressing or providing any information about sea turtles or their navigation methods. There is no indication or mention of sea turtles or their relationship to the Earth’s magnetic field.