Input: Task: corroborate the claim using the given text. Claim: Urban green spaces contribute to mental health and well-being. [SEP] Text: The importance of integration of within and around the built environment is witnessed through these case studies. Hence, even small efforts by policymakers for strengthening the bye-laws definitely bring cognitive development for the children at as well as reduce the urban stress and enhance the productivity at Workplace. Moreover, architects and policymakers should also put an effort to bring the associative factors such as symbolic, experiential, active, and social spaces while designing as mentioned in the framework responsible for physical, mental, and social well-being, which act as three pillars for cognitive development in children. This also helps in the formal stage development, preoperational and operational stage development as well as sensorimotor stage development in children. The learnings of the case studies as mentioned in Table 1 are indicating the same. School CampusBenefits12-Plantsoen, NetherlandsPsychological benefitsThe space within the Centre periodically acts as a marketplace to share the women’s creations. Such place gives the tourists opportunities to venture to the nearby dunes to experience the sunset and harness the locational criteria of the Centre. Bringing a natural playscape like this into the heart of the neighborhood increases children’s daily contact with nature, an important factor for a healthy childhood. Ecological benefitsThe elliptical shape of the structure helps in creating a cooling panel of airflow in the hot and dry environment. The wall permits air to flow through the building and keep the Sun and sand out. Trees were selected based on their morphological characteristics to serve specific functions. A small hollow, which holds rainwater act as a sustainable drainage system in the courtyard. Social benefitsRenowned female artists, designers and education advocates are invited to create artwork, host events, and present installations. Students also learn to read, write, and develop ancient skills distinctive to the region. A small pavilion stores playing equipment, which can be used by the local children. Sports equipment are also available for the local sports coaches. Table 1. Comparative analysis of case studies: School campus. The associative attributes such as energy efficiency, work-leisure congregation spaces, green-blue spaces in its various forms and scales should also be incorporated for better competence and productivity of the employees (as mentioned in Table 2). The integration of these spaces not only improves attention and memory, but also reduces the number of thoughts that lead to depression. Additionally, it also encourages physical activity, which can be considered as a valuable long-term investment in improving health. [SEP] Justify:

Output: The text corroborates the claim by discussing how urban green spaces can improve attention and memory and reduce thoughts that lead to depression. It emphasizes the cognitive and social benefits derived from integrating nature into urban environments, which supports the assertion that such spaces are beneficial for mental health and well-being.


Input: Task: contrast the claim using the given text. Claim: Everyone who uses social media will experience mental health problems. [SEP] Text: 1Convergence and divergence among social construction of mental illness, Person-to-Interview Study, 2018–2021 (N = 2559)Full size imageTable 2 Categorization of the patterns of convergence and divergence in self, social network and medical professional perspectives on individuals’ mental health status, Person-to-Interview Study, 2018–2021 (N = 2559, unweighted)Full size tableOne group met the thresholds for a CAT-MH™ diagnosis; however, neither they nor their social network members reported a problem. These are The “Unmet Needers” (8. 7%) who, from the individual and public health perspectives, meet clinical criteria but are unrecognized in the community. The “Self Deniers” (2. 9%) do not report poor or fair mental health themselves but are seen by both their social networks and identified by the CAT-MH™ as meeting criteria. The “Network Deniers” (6. 0%) report that they have a mental health problem which is supported by the CAT-MH™ assessment but denied by their social networks. The “Worried Well” (4. 9%) report poor mental health but neither their social network nor the professional construction support that view. Nearly the same number of respondents are The “Network Coerced” (4. 6%) who are at risk for encountering pressure to seek out mental health care by their social network when neither they nor the CAT-MH™ identify a problem. Like some of the individuals in the Alang and McAlpine study [60] they may follow a ‘coerced’ pathway to mental health care but fail to meet diagnostic criteria if they do. Finally, only a small percentage of individuals fall into The “Prodromal” (1. 4%) where the community constructions of self and social network see a problem but no clinically-relevant condition is detected at this point by the CAT-MH™. Correlates of the construction of mental health problemsThese eight patterns are used to examine the association with socio-demographic characteristics. Table 3 reports the overall tests of significance across social construction patterns of mental health. Age, gender, and education discriminate across all the patterns while neither minority nor rural status do so. [SEP] Justify:

Output: The text contrasts the claim by detailing research findings that highlight various patterns of mental health status among individuals, indicating that not everyone who might engage in social interaction or use social networks experiences mental health problems. For instance, terms like 'Unmet Needers' and 'Self Deniers' illustrate that some individuals meet clinical criteria for mental health issues yet do not recognize them or report problems. This suggests a complexity in mental health experiences that contradicts the blanket assertion that all social media users will face mental health challenges.


Input: Task: pass the claim using the given text. Claim: Insect pathogenic viruses like baculoviruses can be tailored for specific pest species. [SEP] Text: of Control 30(1):1 -Beaudin V, Derbyshire R, Fernandez--’-deWaard JR. 2015. Biodiversity inventories in high gear: DNA barcoding facilitates a rapid biotic survey of a temperate nature reserve. Journal 3:e6313 Tukey J. 1958. Bias and confidence in not quite large samples. of 29:614. 2020. 2022. A metabarcoding tool to detect predation of the honeybee Apis mellifera and other wild insects by the invasive Vespa velutina. Journal of 95(2):997-1007. 2019. Species delimitation in the presence of strong incomplete lineage sorting and hybridization: lessons from ophioderma (Ophiuroidea: Echinodermata) and Evolution 131:138-148 Wharton RA. 1993. Bionomics of the braconidae. of Entomology 38(1):121-143 Wolda H. 1988. Insect seasonality: why? of Ecology and Systematics 19(1):1-18. 2022. Field investigation- and dietary metabarcoding-based screening of arthropods that prey on primary tea pests. Ecology and Evolution 12(7):e9060 Young RG, Milian-Garcia Y, Yu J, Bullas-Appleton E, Hanner RH. 2021. Biosurveillance for invasive insect pest species using an environmental DNA metabarcoding approach and a high salt trap collection fluid. Ecology and Evolution 11(4):1558-1569. 2012. Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods in Ecology and Evolution 3(4):613-623. 2022. DNA metabarcoding unravels unknown diversity and distribution patterns of tropical freshwater invertebrates. 67(8):1411-1427 a question Learn more about Q&A a link Alert Loading. . . Just enter your email Tools & info history See citing 4. 6MB) Save to in download will start in a moment. . . Cancel × Report a problem Common use cases Typos, corrections needed, missing information, abuse, etc Our promise PeerJ promises to address all issues as quickly and professionally as possible. We thank you in advance for your patience and understanding. [SEP] Justify:

Output: The text does not provide any relevant information regarding the claim about insect pathogenic viruses like baculoviruses and whether they can be tailored for specific pest species. Instead, it discusses various studies related to biodiversity and insect ecology without mentioning baculoviruses or their application in pest control.


Input: Task: corroborate the claim using the given text. Claim: Urban green spaces contribute to mental health and well-being. [SEP] Text: WorkplaceBenefits34benefitsGreen terraces not only permit one to figure outdoors and stimulate interaction amidst the flora-fauna, but also offer trails to relish leisurely walks. The component of surprise that changes because of the change in the atmosphere around revitalizes the senses. The garden areas of varied scales are sited throughout the campus and act as quiet pondering spots for workers. Instead of offering employees’ traditional cubicles and conference rooms, the campus is set up with fluid spaces. Ecological benefitsThe terraces affect the micro-climate on the site, along with the water in the bio-lake. The landscaped terraces covered in grass, insulate the building, thereby reducing the heat load and creating comfortable conditions. Large trees provide natural shading and make the outdoors comfortable during hot summers. The landscaped exterior offers environmental sustainability and a low-water landscaping plan. Natural light and fresh air are intelligently incorporated in the design. Perimeter bioswales and bioretention zones collect stormwater runoff for onsite treatment. ComfortA green wall on the western façade of the building shields the usable areas from harsh western sun. While the green buffer zone in between green wall and usable areas cuts off the radiation, the terraces conjointly offer insulation to workplace areas below. The work areas for cryptography engineers, accommodates 3–4 workers where they can have productive work singly as well as collaboratively. The rooms are set on exterior of the building to welcome natural light. Table 2. Comparative analysis of case studies: Workplace. 1. Moss, Study: A Biophilic Workspace, Moss Amsterdam, 07-Oct-2021. [Online]. Available from: [Accessed: February 10, 2022]2. . Interface of indoor and outdoor space in buildings a syntactic comparison of architectural schools in Istanbul. In: Proceedings of the 7th Edited by Daniel Koch,. Stockhol: ResearchGate; 2009. p. 1323. Rugel E. : Pathways, Impacts, and Gaps, for (NCCEH). 20154. Cognitive Development—problems, Healthofchildren. com, 2022. [Online]. Available from: [Accessed: February 10, 2022]5. Bochart S. of Biophilic Design—Elements. Shepley Bulfinch. [Online]. 2014 [Accessed: February 10, 2022]6. . [SEP] Justify:

Output: The text corroborates the claim by highlighting how green terraces and landscaped areas in the workplace promote interaction with nature, provide quiet pondering spots, and create a stimulating environment that revitalizes the senses. The mention of leisurely walks and the presence of natural light and fresh air suggests significant benefits for mental health and well-being, supporting the claim regarding the positive contributions of urban green spaces.


Input: Task: contrast the claim using the given text. Claim: Everyone who uses social media will experience mental health problems. [SEP] Text: These findings suggest that, despite indicators of need, that individuals may be unable to recognize mental health problems; and even if they do, that those around them may tend to deny the problem [66, 67]. Those with advanced education are more likely to be among The “Well” and The “Network Coerced” than The “Sick”, “Network Deniers”, “Worried Well” or “Unmet Needers”. They are also more likely to the among The “Self-Deniers” than The “Sick. ” For those with college education, the social construction of lay, network and professional groups all come together to reflect better mental health of those with higher status. In addition, their higher documented use of service not only reflects their own recognition of problematic versus non-problematic mental health; but, if they deny problems (although they are significantly less likely to be deniers), their social networks are likely to provide a greater push into treatment, decreasing unmet need. Older individuals are less likely to be in any other profile than The “Well” (leftmost cell, Fig. 2b). In line with this, they are more likely to be in most other profiles than The “Sick” or “Prodromal” (sixth cell). They are less likely to be pushed into care by their networks who see need when they don’t (second cell from the left) or to deny need (fourth cell from the left). However, if they perceive mental health problems, they may risk unmet need (third cell from the left) or be seen as among The “Worried Well” (fifth cell from the left). DiscussionWe deployed representative, population data to examine how individuals’ perceptions of their own mental health status aligns with or diverges from their social networks or psychiatry’s diagnostic rules. The greatest alignment across all three perspectives, which may provide relief from sociomedical concerns, occurs among those individuals who do not have mental health problems. Almost two thirds of the sample show concordance of individual, social network, and psychiatric perspectives that their mental health is fine. While much lower (~ 9%), The “Unmet Needers” are the second highest category. Here, diagnostic criteria suggest need but neither individuals nor their networks agree. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity of individuals' perceptions of their mental health status in relation to their social networks and professional diagnostic criteria. It mentions that a significant portion of individuals, especially those without mental health problems, feel that their mental health is fine, suggesting that not everyone who uses social media experiences mental health issues. Moreover, it details how higher educational status and age correlate with better mental health perceptions, which further implies that social media usage does not inherently lead to mental health problems.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: PeerJ Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario Ahmad Musyafa1,2, Ying Gao1, Aiman Solyman3, Siraj Khan4, Wentian Cai1, Khan1 1School of and Engineering, University of Technology, Guangzhou, China 2Department of 3Department of Computer Science, University of Milan, Milan, Italy 4School of Software Engineering, University of 10. 7717/peerj-cs. 2122 Published 2024-07-05 Accepted 2024-05-21 Received 2023-10-24 Giancarlo Sperlì Algorithms and Analysis of and Speech, language processing, Neural machine translation, Grammatical error correction, Dynamic decoding Copyright © 2024 Musyafa et al. is an open access 2024. Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario. PeerJ 10:e2122 The authors have chosen to make the review history of this 1. Figure 1: Visual representation of the proposed SiaGEC framework. Download full-size image DOI: 10. 7717/peerjcs. 2122/fig-1 Confusion method The proposed confusion method is a combination of modified versions of two approaches. The first technique named Inverted-Spellchecker was introduced by Grundkiewicz, Junczys-Dowmunt & Heafield (2019) and the Patterns+POS method was proposed by Choe et al. (2019), both approaches achieved significant improvements in GEC in English and other Latin languages. However, the constructed data was used to train the GEC model, which left side of Fig. 1 provides a graphical representation of the process of generating synthetic data, and the following subsections provide detailed information. Inverted spellchecker We implement a modified version of the Inverted-Spellchecker that was proposed by Grundkiewicz, Junczys-Dowmunt & Heafield (2019). In order to generate a confusion set, we utilized the Aspell-spellchecker technique in the original 1. In contrast to the previous system (Grundkiewicz, Junczys-Dowmunt & Heafield, 2019), we made some modifications by changing the probability level of errors in system operation. [SEP] Justify:

Output: The text does not address the claim regarding semantic memory and its role in understanding meanings of words and sentences. Instead, it discusses various methods related to grammatical error correction and the generation of synthetic data, which is unrelated to the concept of semantic memory.


Input: Task: corroborate the claim using the given text. Claim: Urban green spaces contribute to mental health and well-being. [SEP] Text: Gardening as a mental health intervention: A review. Review Journal. 2013;18(4):214-225. DOI: 10. 1108/mhrj-02-2013-00077. Kaplan D. Online teacher training of cognition and learning in education. Psychology. 2017;08(03):373-386. DOI: 10. 4236/psych. 2017. 830238. Cherry K. Jean Piaget’s life and contributions to psychology. Verywell Mind. 2020;8:362 [Online]. Available from: [Accessed: February 10, 2022]9. . Academic greenspace and well-being—Can campus landscape be therapeutic? Evidence from a German university. Wellbeing, Space and Society. 2021;2:100003. DOI: 10. 1016/j. wss. 2020. 10000310. et al. Exploring pathways linking greenspace to health: Theoretical and methodological guidance. Environmental Research;158, 2017:301-317. DOI: 10. 1016/j. envres. 2017. 06. 02811. . Impact of urban nature on executive functioning in early and middle childhood. Environment and Behavior. 2016;49(1):3-30. DOI: 10. 1177/001391651560309512. Khazan O. How nature makes kids smarter. The Atlantic. 2022;37:18 [Online]. Available from: [Accessed: February 10, 2022]13. Whitely O. 8 Benefits of Spaces: Whiteley, Oneonwhiteley. co. za. [Online]. 2017 Available from: [Accessed: February 10, 2022] information 1. Introduction2. Methodology3. Models and theories4. Benefits of green spaces5. Case studies6. Conclusions and discussions 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation © 2022 The Author(s). Licensee IntechOpen. This 3. 0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Continue reading from the same book by by Castanho Published: 19 October 2022 0. [SEP] Justify:

Output: The text supports the claim as it references various studies and reviews that indicate the benefits of green spaces on mental health and well-being, including a review on gardening as a mental health intervention, evidence from a German university regarding greenspace and well-being, and explorations of how urban nature affects health. All these references suggest a positive relationship between urban green spaces and improved mental health outcomes.


Input: Task: contrast the claim using the given text. Claim: Everyone who uses social media will experience mental health problems. [SEP] Text: From a psychiatric perspective, programs of individual and community-based education continue to be necessary for such individuals to receive care. However, in absence of “signs” of mental illness (e. g. , blood tests) and none on the horizon [13], overdiagnosis, over-medication, and stigma may result if the individual and social network perspectives better map the situation here. As noted earlier, those seen as having mental health problems from all perspectives comes close to US population estimate of serious mental illness in any one year but far away from NIMH prevalence figures [59]. Overall, The “Sick” are more likely to be younger, women, people of color, and with lower levels of education. This convergence of need reflects known epidemiological groups at risk for mental health problems, and for health care disparities. The more complicated profiles suggest pockets of concern for either under or over diagnosis. On the positive side, findings suggest that as individuals age, they are less like to be at risk for or deny current/future mental health problems or experience coercion from their social networks. However, if older individuals are in The “Sick” category, they may be at risk for unmet need or to have problems denied by social networks or psychiatrists. In more than a modest percentage of cases (~ 30%), there is a mismatch of perspectives. Here the concerns about whether and how to respond are more difficult, in part because there is no clear homogeneity of possible reasons nor clear courses of action. For one female subgroup, need is denied even when the social network and psychiatry say otherwise. Another subgroup of women may be coerced into care by their networks when neither they nor psychiatry see need. For youth, one subgroup is more likely to either deny need; another is part of The “Worried Well”, and a third is at risk for coercion into care. People of color are more likely to have unmet need, in one subgroup profile, because neither they nor those around them recognize psychiatrically defined problems. However, among a different subgroup, people of color are less likely to have unmet need than to be coerced into care by those around them, putting them at risk for service use that does not result in a diagnosis [60]. Among another subgroup, claims of need by people of color for care are rejected by both the health care system and their social networks. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexities of mental health diagnoses and the variability in experiences among different demographic groups. It explains that younger individuals, people of color, and those with lower education levels may have differing perspectives on their mental health needs, indicating that not everyone will experience problems. Furthermore, it highlights subgroups that experience denial of need or coercion into care, suggesting that mental health issues are not universally experienced by all social media users.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: To generate synthetic training examples, we execute the subsequent steps: Initially, the system infuses artificial errors into several words within the original error-free sentences sampled from a monolingual corpus of the web crawl data (Wenzek et al. , 2019). Additionally, the system determines the number of words to modify in each sentence, drawing from the word error rate in the development dataset with a normal distribution average of 0. 15 and a standard deviation of 0. 2. Furthermore, each selected word is applied to one of the following operations: (1) substitution of an original word wi with a random word from the confusion set (with 0. 2 probabilities), (2) deletion of an original word wi (with 0. 2 probabilities), (3) insertion of wi with random words (with 0. 2 probabilities), and (4) swapping wi with adjacent words (with 0. 2 probabilities). In addition, to improve the model’s ability to correct misspellings, we perform the above operation with the same probability at the character level at a rate of 0. 1 in the source sentence to generate spelling errors. Examples of erroneous sentences, encompassing both synthetic and spelling errors, are displayed in Table 2. Table 1: Examples of spellchecker confusion outputs. Source words Confusion sets short sort, shut, source, shoot, sound, shirt, SO OUT, sold paper Papers, piper, pepper, peeper, pearl, peer, people leave Lift, LEave, left, LOVE, life, leaves, leaf, LIVE and end, AnDs, ON, ant, ands, ends, ads, in talking walking, tooling, whaling, taking, too king, looking or ORR, ore, orb, oral, oar, Ro, our used eased, issue, issued, sued, assumed, assured DOI: 10. 7717/peerjcs. 2122/table-1 Table 2: An artificial example of erroneous sentences, words in bold refer to incorrect words. Original input They have read many books and papers and have published many papers in international and highly reputable journals by collaborating with many researchers around the world. Synthetic errors They have road many books, peeper end have published money papers on the international and height reputable journals by a collaborating with money researchers around the word. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the generation of synthetic training examples and the manipulation of words in sentences for error correction without addressing semantic memory or its role in understanding the meanings of words and sentences.


Input: Task: corroborate the claim using the given text. Claim: Tree-planting initiatives in cities can reduce urban heat island effects. [SEP] Text: So, the propensity for catastrophe is higher. 3But we also have the problem that most of the population is living in cities now. As you walk around, imagine that since 2008, half of the world is living in cities, but in the next fifty years we’re going to have 80 per cent of all people living in cities. This is why this is getting more and more attention. So even the researchers and leading thinkers that are working on addressing rural issues are all looking into the city. For example, the Food and (FAO) is now looking at cities as well and launching programmes like the Initiative. Suddenly, even those who’ve dedicated their lives, their research, their work and their profession to the rural areas see that 80 per cent of the world is going to live in urban areas. This means that the current state of affairs cannot be sustainable. It’s not just climate change that would impact us; it’s also the way we’re going to design our cities. What we have now happening around the world is a quick response by solving the problem itself or breaking down the problem and solving each issue. For instance, how would you address the HIE in cities? The answer might be partly through recovery of surface vegetation, a sort of urban nature. But we’re not just talking about the surface, the streets or the spaces between buildings but also about rooftops. This is a concept that’s spreading like wildfire now around the world, in many cities. It turns out that the more attention you give to the materials you use on your surfaces, the better your city will cope with this phenomenon. 4Then some new concept starts emerging, such as heat equity. As you can imagine, this is related to the unjust distribution of the impact of HIEs. This is another issue that cities are now racing to address, such as having special programmes dedicated to vulnerable groups of society that would be most affected not just by climate change but by what we have done to our cities to accelerate or magnify this challenge. Let me move on to the projects that are being done, focusing for the sake of time on one sector as an example. This is a sector that we’re working with a lot in UN-Habitat – sustainable mobility. The question is, what is the relation between sustainable mobility and the HIE? Take a walk after this event. [SEP] Justify:

Output: The text corroborates the claim by discussing the recovery of surface vegetation as a strategy to address the urban heat island effect (HIE). It suggests that urban nature, which includes tree-planting initiatives, is a critical component in making cities more resilient to HIEs, thereby supporting the assertion that such initiatives can help mitigate these effects.


Input: Task: contrast the claim using the given text. Claim: Everyone who uses social media will experience mental health problems. [SEP] Text: It has been found, for example, that the sharing of intimate, self-related information is more accepted in the social media context than in equivalent offline contexts (Christofides et al. , Reference Christofides, Muise and Desmarais2009). Another (co)created norm is that the sharing of negative emotions is more accepted in private (e. g. , WhatsApp) than public social media contexts (e. g. , Instagram; Waterloo et al. , Reference Waterloo, Baumgartner, Peter and Valkenburg2017). And if adolescents do want to share intimate, self-related information on a public social medium like Instagram, they sometimes turn to a Finsta (a account where they can be honest and show their true self) in addition to a Rinsta (a account used to post their positive experiences). Finally, overly emotional expressions on in public social media are considered norm violations (Waterloo et al. , Reference Waterloo, Baumgartner, Peter and Valkenburg2017). The second part of this proposition, that adolescents’ unique experiences within their (co) created social media context can shape the effects of this context, has also received support. For example, message recipients can selectively and autonomously expose themselves to uplifting or depressing social media messages, which may subsequently affect their well-being in unique ways. In a qualitative study of Rideout and Fox (and Fox2018), one adolescent reported: “If I’m feeling depressed, getting on Twitter and seeing funny tweets or watching funny videos on YouTube can really brighten my mood” (p. 20). In this example, a transient dispositional variable (low mood) shaped this adolescent’s selective exposure, which in turn positively shaped their experienced effect (i. e. , a brightened mood). In the same study, another adolescent’s preexisting low mood resulted in an opposite effect of social media browsing (i. e. , a worsened low mood): “Social media makes me feel worse when I’m scrolling through feeds and seeing news headlines and posts about how terrible something is” (Rideout & Fox, and Fox2018, p. 19). And yet another adolescent with a preexisting low mood reacted with selective avoidance: “Usually friends post happy things – getting together with others, accomplishments, bragging. [SEP] Justify:

Output: The text contrasts the claim by illustrating that social media can have varying effects on individuals' mental health. It provides examples, such as an adolescent stating that viewing funny content on social media can brighten their mood, suggesting a positive effect. Additionally, it references another adolescent whose low mood led to a worsened state while scrolling through social media, indicating that not everyone's experience aligns with the claim. This variability demonstrates that social media use does not uniformly result in mental health problems for everyone.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: Spelling errors They hav road many boks, peeper end published money papers on teh international and height reputabele journals by collabroating with money researchers around the word. DOI: 10. 7717/peerjcs. 2122/table-2 Pattens+POS In the second part of InSpelPoS, we introduce the use of pattern and part-of-speech techniques. These techniques generate synthetic noisy data through two distinct strategies: token-based (pattern) and type-based (POS). In the token-based strategy, we utilize a monolingual dataset derived from web crawl data to pinpoint error patterns. We perform text editing on the training data several times, typically not less than five. These edits simulate typical human errors, such as missing punctuation, verb tense inconsistencies, and prepositional errors. The output of these text edits is compiled into a dictionary that captures common edits made by human annotators on the in-domain training set. To generate realistic noise (synthetic errors), we apply this dictionary in reverse to grammatically correct sentences, forming a parallel corpus. In the type-based strategy, the system creates noise scenarios based on prior knowledge of the token type. We introduce noise based on POS, concentrating on three types of token errors: verbs, nouns, and prepositions, following the approach proposed by Choe et al. (2019). Each token type is altered based on its common errors without changing its original type. For instance, prepositions are swapped with other prepositions, nouns switch between singular and plural forms, and verbs are replaced with their inflected forms. In practice, we check if each token in the original training set is present in the edit pattern dictionary (first strategy). If it is not, we then resort to the type-based approach (second strategy). This artificial data offers a diverse range of realistic noise scenarios, thereby boosting the effectiveness of the noising function. Dynamic decoding The proposed SiaGEC framework uses dynamic decoding in the decoding process, which is an algorithm capable of simultaneously generating a token and computing the subsequent token probability by incorporating the lexicon and the character models. In this way, by incorporating both models, the algorithm dynamically adjusts subword segmentation during generation. [SEP] Justify:

Output: The text does not address the claim about semantic memory and its involvement in understanding word and sentence meanings. Instead, it focuses on spelling errors and synthetic noise generation techniques in a computational context, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Tree-planting initiatives in cities can reduce urban heat island effects. [SEP] Text: All short- and long-wave fluxes are affected by the special properties of the atmosphere and the surfaces of cities, and the same is true for the net all-wave radiation balance that effectively drives the urban energy balance (Oke 1988). All of the instruments of radiation measurements, their calibration, the data correction, and most of the field methods are the same for urban environments as for open country sites. The calibration of equipment and instruments during intercomparison periods is essential to ensure the quality of the data. Sensor networks frequently contain low-cost, nonstandard sensors, and as such all equipment needs to be tested against a traceable “standard” instrument. Ideally, equipment should be calibrated at a national standards and calibration lab, ensuring the reliability of results and allowing for comparisons with other equipment calibrated to the same standard. Documented quality assurance and quality control (QA/QC) procedures must be used in order to provide end-users with high-quality data. Consideration also needs to be given as to where the data are initially stored and processed (including procedures for ensuring that servers are secure and backed-up), archived long-term, and accessed by end-users. Communication via informal methods such as websites is important for providing information to a variety of stakeholders. Many urban sensor networks have websites through which data can be visualized and downloaded on request. Calibration processes in a laboratory imply that, under the same circumstances, parallel measurements result between sensors and reference tools. Sensor calibration should help meet the requirements described above in discussing the data quality issues facing institutions that are considering operating an automated surface weather station network. Calibration and maintenance processes should be declared in quality assurance documents for the network. Data from urban observations should be recorded a well-defined file format, where the header of file contains the station name, observing period, observing element, and units and frequency of observations. The end-data users (researchers, general public, schools) are able to use these records for their own purposes. 8 The Central--Atlas (UHI Atlas) is a tool for a presentation and exploration of different factors influencing urban heat island phenomena in the area. It can be used to limit the temperature increase in cities by establishing proper short-term and long-term mitigation, risk prevention and management activities (Komac and Ciglič 2014; Ciglič and Komac 2015). [SEP] Justify:

Output: The text corroborates the claim by mentioning that the UHI Atlas is a tool that can help limit temperature increases in cities through proper short-term and long-term mitigation and management activities. This suggests that interventions, such as tree-planting initiatives, are part of the strategies to address urban heat island effects, aligning with the claim.


Input: Task: contrast the claim using the given text. Claim: Everyone who uses social media will experience mental health problems. [SEP] Text: I don’t always want to see it when I’m feeling down about myself so I stay off social media” (p. 20). These qualitative finding illustrate the complex nature of the associations between preexisting disposition (i. e. , low mood), selective exposure to social media messages, and postexposure mood. Mood-induced selective exposure to social media messages can enhance mood (adolescent 1), worsen mood (adolescent 2), and it can lead to selective avoidance (adolescent 3). Such unique differences have also been reported in two recent experience sampling studies by Beyens et al. (Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg2020, Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg2021), who found considerable differences in experienced effects of social media use. In one study, they found that 46% of the participating adolescents felt better after social media browsing in the past hour, while 44% did not feel better or worse, and 10% felt worse after such use (Beyens et al. , Reference Beyens, Pouwels, van Driel, Keijsers and Valkenburg2020). Such uniquely experienced social media effects also seem to hold for message senders. Several studies have shown that message sending (e. g. , posting) can improve the well-being of message senders (Verduyn et al. , Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017), a result that has often been explained by the positive feedback that message senders receive (Verduyn et al. , Reference Verduyn, Ybarra, Résibois, Jonides and Kross2017). However, social media–induced improvements in well-being can also occur without any involvement of fellow users (Pingree, Reference Pingree2007; Valkenburg, Reference Valkenburg2017). Self-expressions on social media, especially when their intended audience is sizeable, may lead to internalization of these self-expressions, for example, via self-perception. Self-perception theory (Bem, and Berkowitz1972) argues that individuals infer their internal self-concept from retrospectively observing their own overt behavior. [SEP] Justify:

Output: The text contrasts the claim by highlighting the complex effects of social media use on mood and mental health. It presents evidence that social media can enhance mood for some users (e.g., 46% of adolescents felt better after browsing), while it may worsen mood for others (e.g., 10% felt worse). Additionally, the text mentions that message sending can improve well-being, further indicating that social media use does not uniformly lead to mental health problems.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: The probability calculation for each sub-word is described in the following equation: (5) p s n | s < n x ≈ p s n | π s < n x = p s n | y < m x where s<n is a sequence of subwords that is converted by concatenation operator π(s<n) into the raw character y<m of preceding subword sn. The equation of p(sn|y<m, x) is based on incorporating the character and the lexicon as follows: (6) p s n | y < m x = g m P c h a r s n | y < m x + 1 − g m P l e x s n | y < m x. Equation (6) above states that the subword probability is obtained from the combination of character and lexicon probabilities. In the dynamic decoding process, we model the subword boundary decision explicitly. For instance, when we create a character, we take into account the character potential on the left (previous) and the right (next) whether to continue or end the subword as done by a naive beam search (Huang et al. , 2019). This aims to avoid bias towards short subword by not directly comparing complete and incomplete subwords. We have four considerations that can calculate the probability of the next character, as follows: Co-End (The left character continues a subword ended by the right character) Co-Co (Both left and right characters continue the same subword) End-End (Both left and right characters end the subword) End-Co (The character on the left concludes a subword while the one on the right continues it (starts a new subword)) We can perform the next-character probabilities and output one character at a time if we apply subword boundaries as follows pco−end > pco−co or pend−end > pend−co. This is as described in Algorithm 1 below. [SEP] Justify:

Output: The text is irrelevant to the claim. It discusses the calculation of probabilities related to sub-words and the process of dynamic decoding, but does not mention semantic memory or its role in understanding meanings of words and sentences.


Input: Task: corroborate the claim using the given text. Claim: Tree-planting initiatives in cities can reduce urban heat island effects. [SEP] Text: AbstractWe monitored crown air temperature, volumetric soil moisture, leaf water potential, rates of gas exchange, and insect pests of green ash (Fraxinus pennsylvanica ‘Marshall’s Seedless’), Austrian pine (Pinus nigra Arnold), and northern red oak (Quercus rubra L. ) trees during a relatively hot summer in Lincoln, Nebraska, U. S. Air temperatures and vapor pressure deficits (VPD) were much higher in trees growing downtown than in nearby trees growing on the University of Nebraska campus. Increased VPDs and reduced soil moisture decreased pre-dawn water potential and gas exchange of ash and oak trees on the downtown site compared to trees on campus. Green ash trees downtown had more damage from lilac borers (Podosesia syringae (Harris) (Sesiidae)) than did trees on campus. Aphids (Aphidiae) and lace bugs (arcuata (Say)) appeared to be more numerous on oak trees growing downtown than on those growing on campus. Cultural treatments that improve soil moisture availability, such as irrigating, increasing planter size, and mulching, are especially critical on heat island sites and may reduce tree stress, pest damage, and mortality. Key WordsTree moisture stressurban heat islandplant water potentialnet photosynthesisinsect damageUrban tree planting programs are widely promoted as a means to reduce the effects of warming of urban environments caused by absorption, advection, and re-radiation of heat from streets and buildings, commonly referred to as the urban heat island effect. Temperatures in urban areas may be up to 8°C to 10°C warmer than in the surrounding countryside (Davidson 1988). Trees are effective at cooling microclimates through shade and transpiration. However, in urban environments trees face an array of man-made and natural stresses that may reduce their life spans compared to trees in rural areas or natural stands. Although estimates vary, life spans of trees in downtown areas are often less than years (Berrang et al. 1985; Gilbertson and Bradshaw 1985; Nowak and McBride 1991). Increasing our understanding of the relation of urban site factors to tree stress and health is critical for urban tree management, tree selection, and success of urban tree planting programs. [SEP] Justify:

Output: The text corroborates the claim by stating that urban tree planting programs are widely promoted as a means to reduce the effects of urban heat islands caused by the absorption, advection, and re-radiation of heat from streets and buildings. It specifically mentions that trees are effective at cooling microclimates through shade and transpiration, therefore supporting the idea that tree-planting initiatives can mitigate urban heat island effects.


Input: Task: pass the claim using the given text. Claim: Everyone who uses social media will experience mental health problems. [SEP] Text: If these individuals share positive self-expressions induced by the positivity norm in public social media, these individuals may, due to a desire for a consistency between their overt behavior and their self-concept, adjust their self-concept to match their behavior. For a discussion of self-effects in social media, and the mechanisms that may explain such effects, such as cognitive reframing, biased scanning, and public commitment, see Valkenburg (Reference Valkenburg2017). as a Micro- and second proposition of a transactional affordance theory of social media uses is that the social media context is a micro-level context, in which effects on participants are more likely than in the mass media context. Bronfenbrenner was one of the first to conceptualize the relationship between individuals and their social contexts. He distinguished between four types of contexts: the micro-, meso-, macro-, and exosystem (Bronfenbrenner, Reference Bronfenbrenner1979, and Bronfenbrenner2005). The microsystem involves direct interactions of the child with their most proximal circle, such as the family, peer group, or neighborhood. The mesosystem represents the possible interactions among these microsystems (e. g. , between the family and peer group), whereas the macrosystem refers to the overarching culture or subculture of children. Bronfenbrenner’s fourth context, the exosystem, refers to social contexts that do not allow the child as an active participant but that have the potential to affect the child. An example of an exosystem is the work context of one of the parents of the child. A child cannot actively participate in this context but can in many ways be influenced by it. At the time of the development of his theory, Bronfenbrenner identified the mass media as an exosystem because it did not allow for active involvement of adolescents, even though it could shape their experiences. Although valid at the time, Bronfenbrenner (1917–2005) could not have foreseen the rapid developments within the media landscape. If he could have, he would probably have categorized the social media context as a microsystem rather than an exosystem. After all, unlike before, the media landscape now does allow for, and even stimulates, direct interactions among participants. [SEP] Justify:

Output: The text does not directly address the claim that everyone who uses social media will experience mental health problems. Instead, it discusses the mechanisms of self-concept adjustment and interactions within social media contexts, referencing Bronfenbrenner’s theory of social systems. This exploration of social media's role does not inherently support or contrast the claim regarding mental health problems.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: _______________________ Algorithm 1 : The proposed dynamic decoding process_____________________________ 1: Input: x → The source sentence 2: Output: y∗ → The generated output a character sequence concluding with < eos > (end of sentence) 3: Notation: C → A character vocabulary 4: yco : Partial output, last char ends subword 5: yend : Partial output, last char continues subword 6: Yco = argmaxPend−co(Y |X),Yco = [Yco] 7: Y ϵC 8: Yend = argmaxPend−end(Y |X),Yend = [Yend] 9: Y ϵC 10: while yend[−1] ⁄=< eos > do 11: Yco−co = argmaxPco−co(Y |yco,X) 12: Y ϵC 13: Yend−co = argmaxPend−co(Y |yend,X) 14: Y ϵC 15: yco = argmaxP(y) 16: yϵ[yco,Yco−co],[yend,Yend−co] 17: Yco−end = argmaxPco−end(Y |yco,X) 18: Y ϵC 19: Yend−end = argmaxPend−end(Y |yend,X) 20: Y ϵC 21: yend = argmaxP(y) 22: yϵ[yco,Yco−end],[yend,Yend−end] 23: end while 24: y∗ = yend 25: return y∗______________________________________________ Essentially, the decoding algorithm will be more ideal if it makes final segmentation decisions based on the characters before and after the potential subword boundaries during the decoding process. Thus, all possible segmentation in each generation step can be considered properly. The four considerations that can compute the probability of the next character can be formulated as follows: (7) P e n d − e n d Y | y < m x = g m P c h a r Y < e o s > | y < m x + 1 − g m P l e x Y | y < m x. This is the first and simplest case, in which the generated previous character at position m − 1 concludes a subword. [SEP] Justify:

Output: The text is largely technical and discusses an algorithmic process related to decoding, with no mention of semantic memory or its role in language comprehension. Therefore, it does not engage with or address the claim in any meaningful way.


Input: Task: corroborate the claim using the given text. Claim: Tree-planting initiatives in cities can reduce urban heat island effects. [SEP] Text: AbstractTending to local residents, as well as the trees, in neighborhoods can have positive impacts on tree survivorship, community development, and improved relationships between foresters and the public. This study explored resident involvement in tree planting and maintenance projects on vacant lots in Detroit, Michigan, U. S. Thirty-eight individuals involved in follow-up care of tree planting sites were interviewed and surveyed to understand their motivations for involvement and their perception of these local neighborhood greening projects. Underlying motivations include an enjoyment from working with nature as well a strong social motivation. Recommendations are offered for forestry professionals seeking improved interactions with local constituents. Key WordsTree maintenancecommunity involvementmotivationCitizen tree planting projects are not only about getting trees into the ground. They also offer opportunities for forestry professionals to share their knowledge of tree selection, planting, and care with the public and to encourage resident involvement in forest stewardship. For local citizens, these projects provide not only a rapid transformation of their neighborhood but also the chance to learn about caring for trees. Residents soon find out that in addition to the trees, the planting site itself and the equipment also need care. In the process, the benefits extend far beyond the trees. A fuller appreciation of these dynamics could lead to better tree care, a more educated citizenry, and thriving communities. The purpose of this paper is to share findings about resident involvement in tree planting projects on vacant lots in Detroit, Michigan, U. S. , with a focus on the individuals most closely involved with the maintenance of these projects after the planting-day festivities are well past. What sustains their interest in these activities? What is their perspective on the vacant-lot projects and the role they play for the neighborhoods? Greater awareness of volunteer motivations, knowledge, and concerns can facilitate the interactions of forestry professionals with citizens, leading to increased understanding of how to involve local residents in future tree stewardship initiatives. Survivorship of neighborhood trees ultimately depends on local efforts supported and guided by professionals. days are busy, exciting, and active. Photo opportunities abound, because many people want to get in on the action. Planting days offer excitement to many neighborhoods that rarely experience this type of group activity. In addition, the end result can completely change the look of a neighborhood as only trees can. In just a few short hours, a residential block is transformed when street trees are added. [SEP] Justify:

Output: The text corroborates the claim by discussing how tree planting not only transforms neighborhoods but also enhances community involvement and knowledge regarding tree care. While it primarily focuses on social and community benefits, the reference to the transformation of neighborhoods through trees implies potential reductions in urban heat island effects, as tree cover can help in this regard. The emphasis on citizen engagement in tree planting and the subsequent maintenance supports the idea that active urban greening initiatives can lead to broader environmental benefits, potentially including the reduction of urban heat islands.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: ReviewRuth Plackett, PhD Alexandra Blyth, MSc of & :of & 3 2PFUnited KingdomPhone: 44 20 3108 3096Email: ruth. plackett. 15@ucl. ac. ukAbstractBackground: There is some evidence that more social media use is related to poorer mental well-being and that social media use can become problematic when it starts to interfere with a person’s daily life and mental well-being. To address this issue and improve users’ mental well-being, social media use interventions (eg, abstinence from social media) have been developed and evaluated. However, there is limited understanding of the effectiveness of these interventions in improving mental well-being. Objective: This systematic review aimed to synthesize the literature on the effectiveness of social media use interventions in improving mental well-being in adults. Methods: A systematic search (January 1, 2004, to July 31, 2022) was completed across 3 databases in accordance with the (Items for and Meta-Analyses) guidelines. Experimental studies evaluating the impact of social media use interventions on mental well-being in adults were included. Outcomes related to mental well-being, such as depression, anxiety, stress, and loneliness, were included. A narrative synthesis without meta-analysis was completed to summarize the study characteristics and effectiveness by outcome and intervention type. was used to measure the quality of the studies. Results: Of the 2785 studies identified through the systematic search, 23 (0. 83%) were included in the analysis. Many of the included studies (9/23, 39%) found improvements in mental well-being, some (7/23, 30%) found mixed effects, and others (7/23, 30%) found no effect on mental well-being. Therapy-based interventions that used techniques such as cognitive behavioral therapy were more effective than limiting use of social media or full abstinence from social media, with 83% (5/6) of these studies showing improvements in mental well-being compared with 20% (1/5) and 25% (3/12), respectively. Depression was the most frequently investigated and improved outcome with 70% (7/10) of the studies showing a significant improvement in depression after the intervention, whereas other outcomes showed more varied results. [SEP] Justify:

Output: The text contrasts the claim by providing evidence that increased social media use is related to poorer mental well-being. It highlights that social media use can become problematic and interfere with an individual's daily life and mental well-being. The systematic review findings indicate mixed effects on mental well-being from interventions aimed at social media use, and furthermore, while some studies found improvements, others revealed no effect. This suggests that the impact of social media algorithms is not always positive, thereby contradicting the claim.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: Then, the single character Y is the next subword probability. <eos > is the special end of the subword token. In this context, Eq. (7) describes that all possible characters Y in vocabulary can be computed and be candidate for the next character. The second case is the modification of the Eq. (7). The case is such that the character m − 1 does not end a subword but represents the final character within the subword, as shown in the subsequent equation: (8) P c o − e n d Y | y < m x = g m P c h a r Y < e o s > | y < l : m − 1 y < l x + 1 − g m P l e x Y | y < l : m − 1 y < l x where y<l:m−1 are the generated character in the current subword and l is the current subword starting position that concluded at m. Equations (7) and (8) still provide candidates when the subsequent character ends a subword. To obtain the probability of the next character that starts and continues a subword, we modify Eq. (7), as the following: (9) P e n d − c o Y | y < m x = g m P c h a r Y | y < m x + 1 − g m ∑ s : s 1 = Y s ≠ Y P l e x s | y < m x In Eq. (9) above, the probability of the succeeding character in the first component is exclusively dependent on the character-level model, excluding token ¡eos¿. Meanwhile, the other component omits all subwords that start with the specified character y(s ≠ Y), because it is a subword that ends with the character m. As a generalization of Eqs. (7) to (8), (9) can also generalize to the next equation, namely the case where the character m continues the subword starting with the previous character. Thus, it produces the following equation; however, the dynamic decoding algorithm process is described in detail in Fig. 2. [SEP] Justify:

Output: The text does not address semantic memory or its role in understanding the meanings of words and sentences. Instead, it focuses on character probabilities and equations related to subword modeling, which are not relevant to the claim about semantic memory.


Input: Task: corroborate the claim using the given text. Claim: Tree-planting initiatives in cities can reduce urban heat island effects. [SEP] Text: The lowest-scoring item, “working there fills up my time,” lends support to the notion that workers are not tending these lots out of boredom or a need to keep themselves busy. Exploring the underlying motivations of local residents who choose to participate in follow-up care of tree planting projects can provide clues that will assist in enhancing local participation in future projects. A chance to work with nature is only one way to encourage local participation. Framing tree planting initiatives as a way to contribute to one’s neighborhood or help one’s community may enable forestry professionals to attract greater participation in local tree care initiatives. Local KnowledgeLocal residents tending tree planting projects have specific knowledge of their neighborhood useful to foresters such as what goes on there, how residents interact with one another, and how the completed project site is used by residents. In addition, through their work on the treed lots, these individuals have gained experience helpful in future neighborhood tree planting and maintenance efforts. During the interview process, maintenance workers spoke candidly and shared details about the steps taken to maintain their local vacant-lot planting project. The information they shared reflected the specifics of the maintenance work (including tree and lawn care) as well as their experience in tending these local neighborhood sites. An overgrown vacant lot can visibly destroy the best intentions of local residents to keep their homes and yards looking neat and trim. Therefore, one of the main objectives of vacant-lot tree planting projects is beautification of the neighborhood. Often the planting project maintenance tasks requiring the greatest commitment of time and energy are mowing grass and picking up litter. Given the fact that many of these vacant lots were sites of illegal dumping and vandalism prior to their conversion to neighborhood oases, it was not surprising to hear maintenance workers describe these tasks with a great sense of purpose: Pick up bits of paper around there and stuff like that, and you know just keep things nice and presentable,. . . keep things looking nice. . . , try to keep it orderly. Keep it clean mostly, that’s the basics. You know, keep it cut and clean. Mowing grass and picking up litter are not directly related to the maintenance of trees; however, regular maintenance of these lots is very important to the survival of the trees planted in them. Lots that do not receive regular mowing and litter removal soon revert to their previous overgrown condition. [SEP] Justify:

Output: The text supports the claim by highlighting the importance of maintaining tree planting projects in urban areas. It discusses how local participation in tree maintenance is vital for the success of these initiatives, which ultimately contributes to the beautification of neighborhoods. The overall improvement of local environments, including through actions like litter removal and grass mowing, is indirectly linked to enhancing tree health and urban greenery. This aligns with the claim that tree-planting initiatives can help mitigate urban heat island effects by increasing vegetation and improving neighborhood aesthetics.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Quality was poor, with 96% (22/23) of the studies receiving a weak global score, mostly for issues related to selection bias because most of the studies (16/23, 70%) used a convenience sampling of university students. Conclusions: This review provides some evidence that social media use interventions are effective in improving mental well-being, especially for depression and when using therapy-based interventions. Further experimental and longitudinal research is needed with representative samples to investigate who may benefit most from social media use interventions. This will help to develop guidance and recommendations for policy makers and clinicians on how best to manage problematic social media use. J Res 2023;25:e44922doi:10. 2196/44922Keywordsreview; social media; mental well-being; mental health; addiction; intervention; mobile phone IntroductionBackgroundOver the past decade, the rates of poor mental well-being have steadily increased in the United Kingdom, with steep increases seen for young adults [1,2]. As of 2022, in the United Kingdom, 1 in 4 individuals aged 17 to 19 years had reported a probable mental disorder, up from 1 in 10 in 2017 [3]. At the same time, social media use is on the rise, and it is estimated that 4. 59 billion people globally used at least 1 form of social media in 2022 [4-6]. Social media generally refers to “internet-based tools that allow individuals and communities to gather and communicate; to share information, ideas, personal messages, images, and other content; and, in some cases, to collaborate with other users in real time” [7]. Social media has significantly changed how people communicate, form and maintain relationships, and perceive each other, and there is concern about how this affects mental health [8]. Evidence on the impact of social media on mental health is conflicting [9]. Some studies report benefits of social media use for mental health, including increased social support, strengthened bonds, and help seeking [10,11]. Other evidence has shown that high social media use has been linked with depression, anxiety, psychological problems, and distress, particularly for young people [12,13]. When social media use begins to interfere with everyday life, it can be considered problematic, with the most severe form arguably termed social media addiction [4,14]. [SEP] Justify:

Output: The text contrasts the claim by presenting conflicting evidence about the impact of social media on mental health. It states that 'evidence on the impact of social media on mental health is conflicting,' suggesting that while some studies indicate benefits, others have linked high social media use with 'depression, anxiety, psychological problems, and distress,' particularly among young people. This indicates that the claim that social media algorithms always have a positive impact is not supported.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: (10) P c o − c o Y | y < m x = g m P c h a r Y | y < l : m − 1 y < l x + 1 − g m ∑ s : s 1 = Y s ≠ Y P l e x s | y < l : m − 1 y < l x. Figure 2: An illustration of Dynamic decoding algorithm and the correction process on each character or token. The ‘-’ sign denotes sub-word boundaries: (1) illustrates the step of generating candidate characters that continue and end sub-words, (2) illustrates the steps that proceed one character forward, and (3) is a segmentation decision completion step. The sequences marked in green are selected before the ones in orange, owing to their higher sequence probabilities. Download full-size image DOI: 10. 7717/peerjcs. 2122/fig-2 Incorporating the Confusion Method, which combines the and Patterns+POS approaches, we generate synthetic data that enhances the training and evaluation of our GEC model. Dynamic Decoding, an integral part of our SiaGEC framework, further refines our model’s performance by adjusting subword segmentation during generation. These methodological components collectively empower our framework to address the unique challenges posed by low-resource languages, showcasing its potential in the realm of Correction. this section, we provide a comprehensive overview of the dataset employed to train our SiaGEC model, the data pre-processing workflow, model configuration, and model evaluation. To begin with, we construct a synthetic parallel training set using data from a monolingual web crawl ( Subsequently, we conducted data pre-processing before model training. Our model’s performance was assessed using the F1 score and Understudy (BLEU) metrics. For more specific information, we refer to the following sub-sections. this work, we sourced the seed synthetic training data from CC100-Indonesian, a monolingual dataset that Facebook AI teams (Conneau et al. , 2020) prepared. The data was collected from January–December 2018 using Commoncrawl snapshots in the CC-Net repository and organized into a single text document with a total data size of 36 GB. We selected the CC100 Indonesian corpus due to its open accessibility and its status as the most comprehensive monolingual Indonesian corpus available. This extensive dataset encompasses 3. 9 GB (source and target). [SEP] Justify:

Output: The text does not provide any information regarding semantic memory, word meanings, or sentence understanding. Instead, it focuses on technical aspects of a GEC (Grammatical Error Correction) model, data preprocessing, and model evaluation, which are unrelated to the claim about semantic memory.


Input: Task: corroborate the claim using the given text. Claim: Tree-planting initiatives in cities can reduce urban heat island effects. [SEP] Text: Local planting projects can provide multiple means for involving residents in stewardship efforts, and continued local participation provides additional opportunities for communication between foresters and their constituency. Highlighting the opportunities that urban tree planting projects offer residents to access nature, as well as to improve the neighborhood, allows forestry professionals to tap additional motivations and increase local involvement. The involvement of local residents in these neighborhood tree planting projects on vacant lots has shown the positive effects of reaching other neighborhood residents. Local maintenance workers are familiar faces in the neighborhood and can provide additional access for residents who are reluctant to interact with municipal employees. Meanwhile, municipal foresters will find willing allies in these maintenance workers whose work is time-consuming and requires considerable input of energy and local resources. Opportunities abound for foresters to share their expertise with workers willing to learn new techniques and improve their effectiveness. In turn, these local residents can share a wealth of information about the neighborhood, local site conditions, and neighborhood concerns that are invaluable to ongoing urban reforestation efforts. The positive impacts maintenance workers have on others in the neighborhood can mean the difference between tree planting projects local residents feel ownership of and those that are seen simply as public property, often ignored or even defaced. By nurturing the relationship with local residents involved in project care, urban forest professionals can begin to understand and address the local concerns of their constituency. Suggestions are offered to forestry professionals seeking a more rewarding interaction with local residents in tree planting projects. Look, Listen, and on at the Neighborhood LevelExtend consideration of site characteristics beyond those of soil, microclimate, and existing infrastructure. Neighborhood social characteristics also factor heavily in the survivorship of urban trees. Observe the neighborhood, noting how residents interact in and around the proposed tree planting site. Ask questions of neighborhood leaders so that the project can be tailored to the strengths of the neighborhood as well as the neighborhood concerns. Understand the various ways residents interact with the site and with one another. Inquire who will be tending the trees in the weeks following planting day and directly involve these residents in the planning process. Build on the Motivations of WorkersThe desire to help or improve one’s neighborhood was rated equally as high as the motivation to work with nature. Neighborhood residents strongly motivated by community concerns may not initially recognize the social rewards of engaging in tree planting and care. By emphasizing opportunities tree planting projects offer for strengthening community social ties, foresters can encourage greater local participation in these efforts. [SEP] Justify:

Output: The text corroborates the claim by discussing how local tree planting projects can enhance neighborhood conditions and foster greater community engagement. It emphasizes that tree planting can improve access to nature and strengthen community ties, which are essential for urban reforestation. Although it does not explicitly mention the term 'urban heat island effects', the implied benefits of increased greenery from tree planting contribute to mitigating these effects, thus indirectly supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Problematic social media use is described as a preoccupation with social media, resulting in distraction from primary tasks and the neglect of responsibilities in other aspects of life [15,16]. Reports suggest that 17. 4% of social media users are affected by some form of problematic social media use, and it is most prevalent in adolescents and young adults [4,17]. Previous research has identified significant positive associations between problematic social media use and depression and anxiety [18]. However, the quality of studies linking social media use and mental well-being is limited by a reliance on unvalidated self-reported measures to assess social media use and by cross-sectional study designs in which causality cannot be inferred [9]. In addition, much of the research on the relationship between social media use and mental health has focused on adolescents, but there is growing evidence that social media use plays a role in adult mental health, particularly for young adults [18]. Use InterventionsStudies have explored the effectiveness of different types of social media use interventions to improve mental well-being, ranging from therapy-based approaches and taking complete breaks from social media to limiting social media use to a few hours a day [19-21]. Therapy-based approaches tend to use therapeutic techniques such as cognitive behavioral therapy (CBT) or group psychological counseling to prompt reflection on behaviors, thoughts, and feelings around social media and consideration of time management; for example, weekly group psychological counseling and CBT diaries have been used to help students manage their social media use by focusing on how they spend their time and how they can improve their relationships and communication skills offline [22]. These types of interventions are thought to bring about behavior change through facilitating self-control and reflection [23]. These therapeutic interventions can help individuals to regulate their social media use and reprioritize their social activity, which may improve well-being [24]. A recent systematic review that explored social media use interventions where participants had time-outs from smartphone use, or what is termed a digital detox, found mixed impacts of these interventions on mental well-being [25]. However, the review did not distinguish between abstinence from smartphone use more generally and specific abstinence from social media and did not explore effectiveness by the different types of social media interventions, such as limited use or full abstinence. Therefore, it is unclear what the effects of different types of interventions are on social media use and on mental well-being. [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that problematic social media use is linked to negative mental health outcomes, specifically mentioning associations between social media usage and depression and anxiety. It highlights that while some interventions aim to improve mental well-being, the overall impact of social media use on mental health is mixed and uncertain, particularly noting that problematic usage may distract from responsibilities and lead to negative consequences for mental well-being.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: The comparison of data between original and synthetic data can be up to five times larger, meaning that the dataset used for training the proposed model (synthetic) is much larger than the original dataset. Finally, we divided the synthetic training data into two sets of categories consisting of a training set of 8,681,648 examples, and a valid set of 2,170,412 examples at 80% and 20%, respectively. The test set was a benchmark from the previous work consisting of 650,129 examples. Data preprocessing In this context, the sustained progress of neural GEC is partly credited to the efficient handling of word representations and subword segmentation (Meyer & Buys, 2023). However, word-level techniques can give rise to numerous challenges beyond vocabulary limitations, whereas character-level methods tend to be computationally demanding and lack semantic richness (Solyman et al. , 2021). To address the problem of open vocabulary, we utilized byte pairing coding (BPE) (Sennrich, Haddow & Birch, 2016b) algorithm to split and represent rare or unknown tokens into subwords (32K subwords) immediately after tokenizing the dataset with SpaCy ( We set the maximum input sequence length to 200 words for both the training and testing phases. This subword segmentation ensures consistency in tokens across training and development sets, ultimately enhancing model performance. In the inference phase, we manually select all the hyperparameters for our model based on our experience with similar models and considering the computational resources available. Model configuration In the experiments, we trained the proposed model using the baseline sequence-to-sequence Transformer architecture (Vaswani et al. , 2017). Besides adapting the decoder block with the dynamic decoding method as detailed in the methodology section, we also implemented several modifications to the baseline architecture, including adjustments to the model size, batch size, number of layers, and head attention. Initially, we adjusted the model size from 512 to 256 and modified the batch size from 2,048 tokens to 256. We then decreased the number of layers from six to four, while retaining the original number of head attentions to eight which plays a significant role in understanding the dependencies between different parts of the input sequence, thereby improving the model’s ability to generate accurate and contextually relevant outputs. In order to boost performance, we adopted the learned positional coding technique rather than the static coding method from the original 0. 1 and 0. [SEP] Justify:

Output: The text does not address the claim regarding semantic memory and its function in understanding word and sentence meanings. Instead, it discusses data handling, model training processes, and technical aspects of neural network architecture, which are unrelated to the concept of semantic memory. Consequently, it is considered irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Nanomaterials are being developed for more effective cancer treatments. [SEP] Text: Published by June 8, 2013 Purchase 2010. Since 2010, she has been a post graduate in College of Chemistry & in Hebei University. She works under the guidance of Zhang. Her work includes the research about the effects of rare earth elements and rare earth nanop2011. During his doctoral training, he pursued projects focusing on basic research in cardiovascular diseases. Currently serving in College of Basic Medicine, Hebei University, he proceeds to study in vascular biology. His major academic work has been published in some leading scholarly journals such as Vasc Biol. , Prof. Zhang obtained his PhD in Chemistry from (2001) and postdoctoral training in the (2001–2003) and of (2003–2006). He joined the in 2007. Now he is the professor of Hebei University, the PhD supervisor, the winner of for Scholars, the member of National Bio-inorganic Committee, the Director of of Hebei Province, and the Director of Center of Hebei University. Prof. Zhang has published over 80 peer-reviewed scientific papers and 12 patents/patent applications. Now Dr. Zhang’s group is developing novel nanomaterial for biological functions and applications. and Xing-Dr. Xing-got his PhD at Laboratory of Biomacromolecules, Institute of Biophysics at CAS. He finished his postdoc at Center for and worked as a at. Dr. Liang worked on Molecular imaging at School of Medicine, before he became deputy director of CAS for of Nanomaterials and Nanosafety, for Nanoscience and Technology of China. Dr. Liang is currently an editorial board member of “Sinica” and “Current Nanoscience” “Advances in Nano Research”. Developing drug delivery strategies for prevention/treatment of AIDS and cancers are current programs ongoing in Dr. Liang’s lab based on understanding of basic physio-chemical and biological processes of nanomedicine. From the journal of Nanomedicine Download 2. 5 W/cm2 and 3. 8 W/cm2, were able to destroy tumors grown on mice (94, 95). In a study conducted by Liu et al. [SEP] Justify:

Output: The text corroborates the claim as it discusses ongoing research and development of novel nanomaterials for biological functions and applications, specifically mentioning programs aimed at drug delivery strategies for the treatment of cancers in Dr. Liang’s lab. This suggests a direct link between nanomaterials and their potential effectiveness in cancer treatments.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: It is also unclear from the literature what specific effects social media use interventions have on adults because much of the previous research in this area has focused on adolescents. Young adults are of particular interest because they have been identified as being vulnerable to problematic social media use in previous research [17,26]. A review specifically synthesizing the evidence on the effectiveness of social media use interventions on adults’ mental well-being will help to identify how best to support those with problematic social media use and poor mental well-being. Synthesizing these experimental studies will also help to understand the relationship between social media use and mental well-being. This systematic review aimed to (1) identify and describe evaluated social media use interventions, (2) report the effectiveness of these interventions on mental well-being outcomes, and (3) evaluate the quality of current research. MethodsThis review was completed in accordance with the (Items for and Meta-Analyses) guidelines [27], and the protocol is available via the Framework [28]. Search StrategyThe search was limited to studies published between January 1, 2004 (because the year 2004 marked the advent of widespread use of social media platforms), and July 31, 2022 [5]. The search strategy was developed by the research team with input from an experienced librarian. Three electronic databases were searched independently: and Web of Science. Papers at full-text screening were used for backward citation chaining, and the reference lists of similar previous reviews were checked for additional 1. Inclusion and Exclusion CriteriaThe inclusion and exclusion criteria listed in Table 1 were applied. Table 1. Inclusion and exclusion criteria. ConceptsInclusion criteriaExclusion criteriaPopulationAdults (age ≥18 years)All countries and any sexAge <18 yearsInterventionInterventions that explicitly aim to reduce social media use through behavioral methods (eg, limiting social media access) or therapy-based methods (eg, CBTa)Interventions that target general internet use, communication methods (eg, texting), and smartphone useComparatorTreatment as usualNo interventionPre- and postintervention comparisonN/AbOutcomeSubjective and objective mental well-being or mental health measures (eg, self-reported measures). [SEP] Justify:

Output: The text contrasts the claim by discussing the unclear effects of social media on adult mental well-being and indicates that much research has focused on adolescents, suggesting that problematic social media use can negatively affect mental well-being. Additionally, the text aims to evaluate the effectiveness of interventions designed to help with problematic social media use, which implies that not all social media use may be beneficial, directly opposing the notion that algorithms have a universally positive impact.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: 15 to mitigate the risk of model overfitting. In our study, we intentionally set the hyperparameters manually. While automated methods for hyperparameter selection exist, we chose manual configuration during experiments for precise control over the models. All experiments were conducted using PyTorch in Python version 3. 9 and executed on two with 25 GB of RAM each, making use of 10. 2. applied MaxMatch scorer (Dahlmeier & Ng, 2012) as our evaluation metric to measure word-level edits within each output, subsequently providing precision, recall, and F1 scores. F1 scores aim to ascertain the precision, which attempts to calculate the percentage of accurate predictions to reveal the proportion of sentences corrected for grammatical errors. In this context, we calculate precision using the following equation, which indicates the percentage of errors that the system accurately corrected out of all the suggested corrections. (11) P = T P T P + F P where TP represents true positives, indicating errors that the system has accurately corrected, whereas FP denotes false positives, indicating errors that the system has not corrected. In this metric, a higher precision score signifies that the system corrects errors very well. On the other hand, we define recall as a good error detection technique, targeting the proportion of true positives to the sum of true positives and false negatives, as in the following equation: (12) R = T P T P + F N where FN represents false negatives, indicating the errors that the system did not detect. In this metric, a good recall score signifies that the system detects errors well. Finally, the F1 score serves as a mean, striking a balance between precision and recall, ultimately indicating the system’s proficiency in detecting and rectifying errors, as the following equation: (13) F 1 s c o r e = 2 ∗ P ∗ R P + R. These metrics are able to assess various aspects of system performance, such as accurate identification and error correction of GEC systems, providing valuable perspectives on the system’s strengths and areas for improvement. Furthermore, we utilized the BLEU-4 score (Papineni et al. , 2002) to measure the similarity between system prediction results and reference data. [SEP] Justify:

Output: The text is irrelevant to the claim regarding semantic memory and its role in understanding language. Instead, it discusses aspects of model performance evaluation related to error correction in a computational context, focusing on metrics like precision, recall, and F1 scores. It does not address the cognitive processes related to semantic memory or how meanings of words and sentences are understood.


Input: Task: corroborate the claim using the given text. Claim: Nanomaterials are being developed for more effective cancer treatments. [SEP] Text: , SWNTs functionalized by PEGylation achieved an optimal blood circulation half-life at 12–13 h, afforded relatively low reticuloendothelial systems (RES) accumulation, high tumor uptake and low skin retention in mice, these results are ideal for cancer treatment in vivo (96). As the sister of CNTs, nanographene can be also utilized in PTT due to the ability of NIR optical absorption (97–99). In the recent work, Yang et al. studied the in vivo behaviors of nanographene sheets (NGS) with PEG coating by a fluorescent labeling method. The results showed high tumor uptake of NGS in several xenograft tumor mouse models. Compared to shows some advantages in vivo behaviors including highly efficient tumor passive targeting and relatively low retention in RES (100). Au NPs include gold nanorods, gold nanoshells and hollow Au NPs and so on, they show largely red-shifted properties which may have great potential in PTT (100–107). Similar to scatter counterpart, Au NPs absorb light millions of times stronger than the organic dye molecules. Nearly 100% absorbed light is converted to heat via the nonradiative properties (108). These features make them a new cohort photothermal contrast agents for PTT (109). It was reported that gold nanoshells demonstrated highly efficient NIR hyperthermia ability due to its strong NIR absorption (102, 110). Huang et al. reported gold nanorods as NIR hyperthermia agents for photothermal cancer therapy (111). Because the surface chemistry is a key factor for the cellular uptake and tissue penetration of nanomaterias (112), it can affect thermal therapy efficiency. Jin et al. have synthesized Au nanorods with three different polymer coatings: cetyltrimethylammonium bromide (CTAB), polystyrene sulfonate (PSS) and poly (diallyldimethylammonium chloride) (PDDAC). Then multicellular tumor spheroid (MCTS) was treated by these different nanorods, after radiated under the NIR laser for 4 min, the thermal therapy efficiency of each kind of Au nanorods is very different. [SEP] Justify:

Output: The text corroborates the claim by detailing various nanomaterials, such as SWNTs, nanographene, and Au NPs, that have been shown to possess properties ideal for improved cancer treatment. It discusses their behavior in vivo, including high tumor uptake and effective thermal therapy efficiency when used in photothermal therapy (PTT), highlighting their potential in developing more effective cancer treatments.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Measures related to mental well-being or those related to factors that may inhibit well-being (eg, stress and loneliness) were also included to encompass the broad definition of mental well-being as a state in which a person can realize their own abilities, cope with the stresses of life, and contribute to their community [29]. FOMOc was also included because it has been found to be associated with problematic social media use and poorer mental well-being [30-32]Outcomes regarding time spent on social media or type of social media useStudy typesRandomized controlled trialsQuasi-experimental designsPre-post studiesCross-sectional studiesQualitative studiesPublication typesPeer-reviewed 10. 02% (279/2785) of the abstracts were screened by a second reviewer, with any conflicts resolved in discussion. κ score was 0. 56, with moderate agreement [33]. The full texts of the remaining 1. The search strategy yielded 2785 research papers, of which, after removing duplicates, 1895 (68. 05%) were selected for title and abstract screening. Of these 1895 papers, we excluded 1862 (98. 26%) based on the inclusion and exclusion criteria and added 9 texts based on the reference list of a previous review. Of these 42 papers eligible for full-text screening, 23 (55%) were included for final analysis. Figure 1. (Items for and Meta-Analyses) flowchart of the systematic search results. Study CharacteristicsStudy characteristics are summarized in Table 2. All studies were published between 2016 and 2022, with the most common locations being the (6/23, 26%), the (5/23, 22%), and China (3/23, 13%). Most of the interventions targeted social media use on general social media sites (10/23, 44%) and Facebook (10/23, 44%), followed by those targeting Instagram (6/23, 26%), Twitter (3/23, 13%), Snapchat (3/23, 13%), TikTok (2/23, 9%), Pinterest (1/23, 4%), and Tumblr (1/23, 4%). Almost a third (7/23, 30%) of the studies targeted the use of multiple specific social media platforms. Randomized controlled trials were the most frequent study design (21/23, 91%). [SEP] Justify:

Output: The text contrasts the claim by discussing factors that inhibit well-being, such as stress and loneliness, which are associated with problematic social media use. Specifically, it highlights that FOMO (Fear of Missing Out) is linked to poorer mental well-being, thus suggesting that social media algorithms may negatively impact mental health.


Input: Task: pass the claim using the given text. Claim: Semantic memory is involved in understanding the meanings of words and sentences. [SEP] Text: In the BLEU score, we used N-gram precision to compute the prediction result with reference data using a maximum size of four n-grams, as in the following equation: (14) B L E U − 4 = B P. e x p ∑ n = 1 4 w n l o g p n where wn denotes weight of n-gram (set to 1 4 ) and pn represents precision. A higher BLEU score indicates greater similarity between the prediction results and reference data, with a score of 1 meaning the predicted and reference sentences are identical. Thus, BLEU scores tend to be high in GEC system evaluation. A brevity penalty (BP) yields a score of 1 when the prediction is overly short. Essentially, if the length of the predicted output exceeds the length of the reference data, the BP will produce a value of 1, as explained in Eq. (15). (15) B P = 1 i f c > r e x p 1 − r c o t h e r w i s e. The evaluation utilized a combination of precision, recall, and F1 score metrics to assess the system’s ability to identify and correct grammatical errors. These metrics provided valuable insights into the system’s strengths and areas for improvement. Additionally, we employed the BLEU-4 score to measure the similarity between SiaGEC predictions and reference data, offering a comprehensive evaluation of the system’s overall performance. These evaluation methods together provide a well-rounded assessment of the GEC system’s capabilities. Result and section presents the experimental findings from our SiaGEC model, which has been trained using synthetic datasets. Additionally, we have explored various seq2seq models, SiaGEC with dynamic decoding, multi-pass decoding, and a case study, which are elaborated upon in the subsequent subsections. Impact of the synthetic data We investigated the effectiveness of InSpelPOS for generating dependable training data across various frameworks. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the BLEU score and evaluation metrics for a grammar correction system without touching upon the role of semantic memory in understanding the meanings of words and sentences.


Input: Task: corroborate the claim using the given text. Claim: Nanomaterials are being developed for more effective cancer treatments. [SEP] Text: In the PSS group, the compact structure of the MCTSs was broken by the Au nanorods thermal therapy, but this phenomenon in the other two groups was not observed. The result indicated that the difference in thermal therapy efficacy was due to the effective heat generated by the Au nanorods with different surface coatings, which were affected by different Au nanorods distribution patterns in the tumor (113). Magnetic hyperthermia (MH)Magnetic hyperthermia is based on the fact that magnetic p5. 5) and anticancer drug (paclitaxel) for cancer treatment. The CNPs have many advantages such as stability in serum, deformability, and rapid uptake by tumor cells. It presents a new strategy in cancer treatment, in which early stage cancer diagnosis, drug delivery, and real-time non-invasive monitoring for therapeutic efficacy are carried out simultaneously (184). Yang et al. have developed a multifunctional nanosystem combining magnetic nanocrystals with therapeutic antibodies and the chemotherapeutic drug DOX, this nanosystem has the ability of imaging, targeting and treating (185). Multifunctional nanomaterials have also been used for in vivo imaging, siRNA delivery and silencing in tumors. Medarova et al. have obtained a nanosystem, which is consisted of magnetic nanop2000. The most interesting thing is that these nanop127. The inner surface of the tube has 50 nm nano-size pores, which can effectively prevent from fibrous tissue infiltration but permeate nutrients and retain neurotrophic factors, while the outer surface has micro-size pores, which can allow vascular ingrowth for effective supply of nutrients and oxygen into the tube. From the investigations of mechanical property, water absorbability, and model nutrient permeability of the tubes, the hydrophilized PLGA/F127 (3 wt%) tube seems to be a good candidate as a NGC for the effective permeation of nutrients as well as the good mechanical strength to maintain a stable support structure for the nerve regeneration (201). The chitosan nanofiber mesh tube may be also a promising substitute for autogenous nerve graft. Wang et al. developed bilayered, deacetylation rate and oriented chitosan tube (202–204). [SEP] Justify:

Output: The text corroborates the claim by describing various applications of nanomaterials in cancer treatment. It mentions a multifunctional nanosystem developed by Yang et al. that combines magnetic nanocrystals with therapeutic antibodies and the chemotherapeutic drug DOX, highlighting its capability for imaging, targeting, and treating cancer. Additionally, it discusses the advantages of CNPs in stability and rapid uptake by tumor cells, presenting a strategy where early cancer diagnosis, drug delivery, and monitoring are achieved simultaneously.


Input: Task: contrast the claim using the given text. Claim: Social media algorithms always have a positive impact on user mental well-being. [SEP] Text: Only 2 (9%) of the 23 studies were pre- and postintervention studies, measuring mental well-being before and after an intervention with no control group [35,36]. There were 3 main types of interventions. Most of the studies (12/23, 52%) evaluated the impact of full abstinence from social media, with the abstinence period ranging from 1 day to 4 weeks. This was followed by therapy-based interventions such as counseling or CBT approaches (6/23, 26%) [22]. Many of the therapy-based types of interventions were self-guided (4/6, 67%) [37-40], with 2 (50%) of these 4 interventions using internet-based app–based platforms [38,39] and 2 (50%) of these 4 interventions using CBT-based diaries to reflect on social media use and time management [37,40]. Of the 6 therapy-based interventions, 1 (17%) used a mixture of in-person peer-based group psychological counseling and an internet-based social media group to share suggestions for alternative activities to social media use [22], 1 (17%) used entirely in-person sessions with training in basic mindfulness skills and values clarification based on acceptance and commitment therapy concepts [35], and 1 (17%) also encouraged limitation of social media use to only a few hours a day [37]. Roughly a quarter (5/23, 22%) of all included studies explored the effect of limiting social media use per day, with intervals ranging from 10- to 60-minute restrictions. More than a third (9/23, 39%) of the included interventions lasted 1 week, with the shortest lasting 1 day and the longest lasting 5 weeks [35]. Table 2. Characteristics of studies and social media use interventions sorted by intervention type. Study, year; countryStudy designIntervention typeSocial media intervention targetIntervention durationComparatorSample size, nAge (years), range (mean, SD)Interventions involving full abstinence from social mediaBrown and Kuss [36], 2020; United KingdomPre-post studyFull abstinenceGenerala1 weekNone6120-49 (24. 40, 4. 95)Fioravanti et al [41], 2020; ItalyRCTbFull abstinenceInstagram1 weekControlc80; IGd: 40, CGe: 40≥18 (25. 05, 4. [SEP] Justify:

Output: The text contrasts the claim as it discusses various studies measuring the impact of social media use on mental well-being through interventions. It highlights that many interventions, including full abstinence from social media and therapy-based methods, are being evaluated due to their potential positive effects on mental health. Notably, it points out that a significant percentage of studies focused on abstinence and restriction of social media use, which implies that social media does not always have a positive impact. This evidence suggests a more nuanced view of social media's role in mental well-being.