Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: Through a gas price feature, the user can find information about a particular location because the person who threatens a patient might be in any of the locations, and it also allows one to find a person’s current location. The link prediction method improves a data transmission’s efficiency (Li et al. , 2022). It is used to route the link prediction based on the behavior of a mobile node. During the person’s movement, various nodes might meet neighboring nodes. When the node moves, the path’s weight increases from the source node to the information interaction node. The weight increases are directly proportional to the quality and amount of data obtained. The weight depicts the coordination between nodes in the social process that enables other nodes to find the optimal path to get information faster and to track the transmission target node as quickly as possible to complete the community structure reconfiguration (Yang et al. , 2022). In the social graph, the probability of a mobile node i from the source node j to interaction node k, as shown below path prediction (Pr) equation, (6) Prijk=WβjmCαjm∑mϵMji(WβjmCαjm) It indicates where the Wβjm denotes the weight of the path and Cαjm The cost of moving from node j to k corresponds to the coefficient of basic parameters. Mji denotes the critical points between nodes i to j. This model assumes that all edges in the social network graph have initial weights. The optimal solution is that the target node must obtain information (Zhou et al. , 2022). The link where people receive information will increase the weight, as shown in the below equations (7) ΔWijk=1Ciwhere,(j,k∈Li) (8) ΔWijk=0,otherwise (9) Wjk=Wjk+∑i=1tΔdenotes the path traversed by the mobile device that from the information source, the length represented as Ci. Where t is the total count of mobile nodes, which indicates the search for the shortest path from the starting point to the information source. Assuming that each edge’s weight will gradually decrease, the more the path is traversed, the greater the weight. Then analyze the reduction mechanism of path weights, as shown in the below equation. [SEP] Justify:

Output: The text contrasts the claim by discussing how data transmission and coordination in a social network setup relies on weights and path optimization rather than guaranteeing data security through blockchain technology. It focuses on the mechanisms of mobile node movement and path weight adjustments, which implies that data security is dependent on the network's structure and conditions, rather than an absolute guarantee provided by blockchain.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: journal of cancer prevention: APJCP, 18(1), 3–9. Louis, D. N. , Perry, A. , Wesseling, P. , Brat, D. J. , Cree, I. A. , Figarella-Branger, D. , Hawkins, C. ,. , Soffits, R. , von Deimling, A. , & Ellison, D. W. (2021). The 2021 WHO Classification of Tumors of the System: a summary. Neuro-oncology, 23(8), 1231–1251. Ribeiro, I. P. , Melo, J. B. , & Carreira, I. M. (2019). Cytogenetics and in Cancer. International journal of molecular sciences, 20(19), 4711. Yu, W. , Zhang, L. , Wei, Q. , & Shao, A. (2020). O6-Methylguanine-DNA Methyltransferase (MGMT): Challenges and in Glioma Chemotherapy. Frontiers in oncology, 9, 1547. Yu, W. , Zhang, L. , Wei, Q. , & Shao, A. (2020). O6-Methylguanine-DNA Methyltransferase (MGMT): Challenges and in Glioma Chemotherapy. Frontiers in oncology, 9, 1547. Qureshi, S. A. , Hussain, L. , Ibrar, U. , Alabdulkreem, E. ,. , Mohammed, G. P. , & Duong, T. Q. (2023). Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. Scientific reports, 13(1), 3291. Yadav, S. S. , Jadhav, S. M. Deep convolutional neural network based medical image classification for disease diagnosis. J 6, 113 (2019). Taye, M. M. Understanding of with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers2023, 12, 91. Sarvamangala, D. R. , & Kulkarni, R. V. (2022). [SEP] Justify:

Output: The text is largely a citation list and does not provide any information relevant to the claim regarding advanced algorithms predicting emotions based on brain scans. It discusses various topics related to cancer, cytogenetics, and medical imaging classification, but does not address the claim in any way.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Upon the creation of the next block in the transaction chain by one of the nodes, it verifies the validity of the new transaction. If deemed valid, the transaction is included in the block, which, as part of the replicated chain, is distributed across all nodes in the network. This process effectively records an outflow from the sender and an inflow to the recipient [5]. 2. 4 Blockchain consensus protocolThe primary function of a consensus protocol is to establish rules governing the addition of blocks to the chain in terms of order and timing. This is crucial for a blockchain to maintain a consistent and unalterable sequence of events, universally agreed upon by all nodes, representing the current state of the database. Furthermore, this event sequence should be immune to censorship, ensuring that no single node possesses unilateral control over the admission of information. Currently, there are two main types of consensus protocols in use [6]:2. 4. 1 Proof of work (PoW)Proof of Work (PoW) serves as the original consensus protocol within the Bitcoin network. This protocol relies on solving intricate logic puzzles that are challenging to solve but easy to verify once completed. It can be likened to assembling a complex jigsaw puzzle with a multitude of tiny pieces — a formidable task to piece together, but a quick glance can confirm if the picture is complete and accurate. In the PoW protocol, the exertion involved in solving the puzzle is termed “Work,” and the puzzle solution is the “Proof of Work. ” Essentially, the ability to verify the correctness of the solution confirms that someone has performed the work accurately. A blockchain utilizing a PoW consensus protocol requires proof for each block added to the chain. As a reward for their efforts, the node receives a specific number of digital tokens, characteristic of the network. In the case of the Bitcoin network, this reward is in the form of Bitcoins, constituting the initial generation of these digital tokens. All circulating Bitcoins and those to be released in the future are generated as a reward for the work executed by the nodes in the block generation process, commonly known as “mining. ” The PoW protocol inherently values the chain with the most blocks as the current “correct” chain, given the greater cumulative work invested in it. Considered highly secure, the PoW protocol presents a significant challenge for any attempt to overwrite or forge a block. [SEP] Justify:

Output: The text corroborates the claim by explaining how blockchain technology works to verify transactions and maintain a distributed ledger. It describes the process of transaction validation and block creation, emphasizing that consensus protocols, like Proof of Work (PoW), ensure that the recorded transactions are transparent and secure across all nodes in the network. This underpins the claim that blockchain technology ensures transparency in cryptocurrency transactions.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: (10) Wjk=(1−p)Eq. (1), the mobile devices are moving from node j to node k from the equation (11) Prijk=(1Wjk)β∑mϵMji(1Wjk)βwhere wjk is the weight of the corresponding path, and the coefficient β is the primary parameter of the model, which is set to 1. Comparing Eq. (6) can see that α = 0, which is inversely proportional to the weight value. If the current memory module is filled, it will be covered one by one according to the order of the arrow’s direction on the way. Additionally, the accuracy of each node is confirmed each time this research acquires three additional nodes. Let us say that one of the memory modules already has the written data. It is assumed that a triangle relationship has been discovered in that situation (Dhieb et al. , 2020). All pathways start with a weight of one. The weight of each direction is raised by one each time a new triangular connection is discovered. The edge’s weight is increased by one if the present pathway is a part of an existing triangle connection. Each path’s importance will not diminish with time. The patient consults a doctor for any biological changes found in the body or due to any disease symptoms. Then, the doctors gather all the information from patients through a lab test, MRI, sensor data, and so on, providing treatment accordingly. All these details, like test reports, sensor data, scan reports, personal information, treatment information, etc. , are stored in a database as electronic records. These records can be accessed by both the patients and doctors and then managed by hospital management. When a patient receives a threatening call or message, they intimate control and track the location of a person through Hybrid microwave transmission for location tracking and MapQuest for providing an exact location in map format to the management, as illustrated in Fig. 4. Results examining resemblances of performance The IoT devices continuously sense and collect data on a user’s health that helps the user’s health status. In this work, the personal health data of a user is collected by wearable devices. [SEP] Justify:

Output: The text contrasts the claim by not providing any evidence that blockchain guarantees data security under all circumstances. Instead, it describes a scenario involving medical data management and collection where various methods are used for data gathering and security (e.g., lab tests, MRI). There is no mention of blockchain in ensuring data security, implying limitations in the data management process and emphasizing that security heavily relies on multiple factors, not solely on blockchain technology.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Convolutional neural networks in medical image understanding: a survey. Evolutionary intelligence, 15(1), 1–22. image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals, Journal, Issue 1178, December 2023, Pages 1287–1294, Krichen, M. Networks: A Survey. Computers 2023, 12, 151. Kayadibi, I. , Güraksın, G. E. , Ergün, U. et al. : AlexNet-Network. Syst 15, 49 (2022). Arkin, E. , Yadikar, N. , Xu, X. et al. A survey: object detection methods from CNN to transformer. Appl 82, 21353–21383 (2023). Yu, F. ; Zhang, Q. ; Xiao, J. ; Ma, Y. ; Wang, M. ; Luan, R. ; Liu, X. ; Ping, Y. ; Nie, Y. ; Tao, Z. ; et al. Progress in the Application of CNN-Classification and Recognition in Growth Cycles. Remote Sens. 2023, 15, 2988. Manimegalai, P. , Suresh Kumar, R. , Valsalan, P. , Dhanagopal, R. , Vasanth Raj, P. T. , & Christhudass, J. (2022). 3D with for Nuclear Medicine. Scanning, 2022, 9640177. (Retraction published Scanning. 2023 Dec 13;2023:9765894) Kerkhof, M. , Wu, L. , Perin, G. et al. No (good) loss no gain: systematic evaluation of loss functions in deep learning-based side-channel analysis. J 13, 311–324 (2023). . , Narayan, V. , Paprzycki, M. , Jaworska, T. , & Ganzha, M. (2023). A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analytics, 100216. Tian, Y. ; Zhang, Y. ; Zhang, H. in Descent in Deep Learning. Mathematics 2023, 11, 682. Kaur, M. [SEP] Justify:

Output: The text does not address the claim about advanced algorithms predicting emotions from brain scans. Instead, it presents a list of studies and surveys related to convolutional neural networks and medical image analysis without specifically discussing emotion prediction or brain scans.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Such an alteration would invalidate previous blocks, requiring the reworking of all prior blocks before a new block could be added. For a successful alteration, a node would need a processing speed exceeding 51% of the combined speed of all other nodes — an event known as a “51% attack. ” While configuring such a system is currently nearly impossible, the potential rise of quantum computers may necessitate a reevaluation of this blockchain protocol in the future. 2. 4. 2 Proof of stake (PoS)Proof of Stake (PoS) represents a more recent consensus protocol model, presently adopted by networks such as Peercoin and BitShares. In contrast to Proof of Work (PoW), the PoS consensus protocol operates on distinct principles unrelated to the high processing power of verification nodes. Instead, preference is granted to nodes with a larger share of the digital (crypto) currency underpinning the process. Advocates of the PoS protocol argue that nodes possessing a substantial stake in the background cryptocurrency exhibit a heightened interest in preserving its market value. Safeguarding their investment becomes paramount, as any network attack or data falsification would erode trust and, consequently, diminish value. Consequently, it is not in their interest to engage in censorship or the falsification of transactions. While PoW necessitates substantial processing power, with technologically advanced nodes operating complex puzzle-solving processes in specialized facilities (often termed computer farms), PoS imposes minimal requirements on processing power. This means that even an ordinary smartphone can effectively contribute to the creation of complete blocks. This characteristic significantly enhances the decentralized nature of the network, as a broader range of entities can afford to participate in the validation process. 2. 5 Types of blockchainBlockchain technology is characterized by its openness and accessibility, allowing for diverse implementations based on specific needs. There are essentially three types of blockchain implementations [7]:Public blockchain: This type constitutes a fully distributed network that is openly accessible to anyone with internet connectivity. Participants can engage at any level, the program code is open and community-maintained, and the validation process can be undertaken by any participant with the requisite technical equipment. Participants typically remain anonymous. Examples include Bitcoin and Ethereum. Controlled blockchain (permissioned or hybrid blockchain): In this model, participants assume strictly controlled roles within the network. The validation process is not open to just anyone; instead, it is managed by pre-selected entities. [SEP] Justify:

Output: The text corroborates the claim by describing the characteristics of blockchain technology, specifically mentioning its openness and accessibility. It discusses the different types of blockchains, including public blockchains like Bitcoin and Ethereum, which are openly accessible and allow for transparency in transactions. The assurance that participants in public blockchains can validate processes aligns with the claim that blockchain technology ensures transparency.


Input: Task: contrast the claim using the given text. Claim: Blockchain guarantees data security under all circumstances. [SEP] Text: The user needs privacy for their health data and access by the doctor and denies access from third parties. Figure 9: Result analysis of accuracy score. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-9 As illustrated in Fig. 10, the number of data and average response time for data submission and the number of data and average response time for data validation, respectively. When the data is submitted to the blockchain ledger, this takes time for the process; this processing time must be less or higher. This is only based on the amount of data. If the data count is the lesser time taken for the process is minor; similarly, if the data is large, then the time taken for the operation or response time might increase. Besides, the validating portion of the blockchain follows this condition. The data submission analysis and validation for the existing decision tree took an average response time of 3,212 ms (on data submission). It took an average response time of 17. 640 ms (on data validation). This average response time is 10% higher than the existing approach. Figure 10: Comparison of various classification methods with execution time. Download full-size image DOI: 10. 7717/peerj-cs. 1308/fig-10 blockchain method and proposed hybrid gradient boost that was suggested concentrated on data transfer to compare several new technologies employing wireless communication. The use of decision trees and blockchain-based decision-making were used to optimize wireless communication. The recommended system produces the following results in terms of prediction accuracy, execution time, execution accuracy, and average response time of data submission and data validation. The suggested hybrid gradient boost methodology offers the best precision value of 99% compared to the prior classification method. The decision tree and the naïve Bayes algorithm have 87 and 84% accuracy rates, respectively. Over the same datasets, the hybrid gradient boost classifier has the fastest execution time, clocking in at 12 ms. Naïve Bayes and the decision tree method have the most prolonged execution durations compared to the other classifiers. However, naïve Bayes and the decision-making strategy had the lowest execution accuracy when using the gradient boost classifier. Performance for the gradient boost classifier, which offers the highest level of precision, is 97. 9%. The research on data submission and validation for an existing decision tree found that average reaction times were 3,212 and 17. 640 ms, respectively. [SEP] Justify:

Output: The text contrasts the claim by discussing the average response times for data submission and validation within blockchain technology. It illustrates that while blockchain can provide certain efficiency metrics, it does not inherently guarantee data security as there could be implications of processing times and varying degrees of performance based on the amount of data, suggesting that security is not absolute under all circumstances.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: , Kaur, N. , & Singh, H. (2014). Adaptive K-means clustering techniques for data clustering. of in Science, Engineering and Technology, 3(9), 1-6. Dawood, E. A. E. , Elfakhrany, E. , & Maghraby, F. A. (2019). Improve profiling bank customer’s behavior using machine learning. IEEE Access, 7, 109320-109327. DOI: 10. 1109/. 2019. 2934644 Mohamed, A. , Rabea, M. , Sameh, A. , & Kamal, E. (2024). Radiogenomic Classification. arXiv preprint arXiv:2401. 09471. DOI:10. 48550/arXiv. 2401. 09471. François, A. , & Tinarrage, R. (2024). Train-in MRI with Homology. arXiv preprint arXiv:2401. 01160. Barakat, M. , Magdy, N. , William, J. G. , Phiri, E. , Confidence, R. , Zhang, D. , & Anazodo, U. C. (2023). : Model for Segmentation in Sub-Populations. arXiv preprint arXiv:2312. 11775. Cheng, D. , Gao, X. , Mao, Y. , Xiao, B. , You, P. , Gai, J. ,. . . & Mao, N. (2023). Brain tumor feature extraction and edge enhancement algorithm based on U-Net network. Heliyon, 9(11). Saeed, N. , Ridzuan, M. , Alasmawi, H. , Sobirov, I. , & Yaqub, M. (2023). MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. arXiv preprint arXiv:2304. 00774. Qureshi, S. A. , Hussain, L. , Ibrar, U. et al. Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans. 13, 3291 (2023). [SEP] Justify:

Output: The text does not provide any information related to the claim about advanced algorithms predicting emotions based on brain scans. Instead, it lists various studies related to data clustering and medical imaging techniques without mentioning emotions or predictive algorithms.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: The program code may or may not be available to participants. Ripple is an example of a controlled blockchain. Private blockchain: Access to private blockchain networks is meticulously controlled and centrally managed. These networks are closed, permitting entry only to pre-approved participants. The program code is closed and not publicly accessible. Private blockchains find utility in consortia and larger corporations that necessitate sharing information and documents among approved entities. In the financial sector, developing a private blockchain is meant to facilitate back-office operations related to clearing and settlement. Advertisement 3. The application of blockchain in worldwide financial systems: its advantages and implications! The intersection of financial systems with information technologies has witnessed a revolutionary shift with the advent of blockchain technology, offering the potential to fundamentally reshape financial landscapes. Several key advantages of blockchain contribute to its transformative impact [8]:Publicly available ledger system: Blockchain operates as a publicly accessible ledger, where all transactions are recorded and verified. This inherent transparency ensures a secure and reliable system, instilling trust among participants. Transaction immutability: Every transaction within the blockchain is authorized and validated by controllers known as “miners. ” Once recorded, transactions become immutable, meaning they cannot be altered or tampered with. This feature enhances security, providing robust protection against malicious intrusions and hacks—common concerns in contemporary financial IT systems. Elimination of intermediaries: Blockchain technology eliminates the need for intermediaries, enabling direct peer-to-peer transactions. Traditional financial systems often involve intermediaries such as banks or clearinghouses, introducing delays and costs. Blockchain streamlines the process, facilitating more efficient and cost-effective transactions. Decentralization: One of the core tenets of blockchain is decentralization. This technology is practically available to everyone, fostering a decentralized network where no single entity holds exclusive control. This decentralization not only enhances accessibility but also contributes to the resilience and security of the system. As blockchain continues to evolve, these advantages pave the way for more inclusive, efficient, and secure financial systems. The elimination of intermediaries, coupled with enhanced transparency and immutability, opens doors to innovative financial applications and services, driving a shift towards more direct and peer-to-peer interactions in the digital economy. The above-mentioned advantages of blockchain technology are manifested in various ways, particularly within financial systems, bringing about transformative possibilities:Cross-border payments: Enable fast, direct one-to-one payments, reducing costs for international transactions. [SEP] Justify:

Output: The text corroborates the claim by discussing how blockchain technology operates as a publicly accessible ledger that ensures transparency in transactions. It emphasizes that the transparency provided by blockchain enhances trust among participants, which aligns with the claim that blockchain underpins cryptocurrency functioning. Additionally, the text mentions that blockchain’s advantages, such as transaction immutability and the elimination of intermediaries, contribute to a more reliable and secure financial system.


Input: Task: contrast the claim using the given text. Claim: No personal data is ever shared by smart home devices. [SEP] Text: Prof. --2020, No 3 Full text (pdf) Aims and on the Issue: to the Protection of Employees' the Associated with Recommendations for e-books & Tools for News and about in the Issue: CounterCraft (UK) Aims and current online newsletter is focused on the topic challenges related to the protection of employees’ home networks and how smart devices changing the threat pattern associated with data sharing. Due the COVID-19 crisis, many security and IT teams have to support and protect employees who must work remotely. The practical recommendations from security experts toward the mitigating risks to enterprise security from home networks and smart-home devices are presented. Eight practical recommendations for remote workers’ protection are given from the security experts. In this respect, a strong policy for all home employees should involve the usage of an antivirus tool on the machines that access the organization’s resources. Consideration should be given to implementing alternative cloud-based workstation monitoring tools. An overview of supported console tools and required licenses to access non-domain computers should be done. A quick decision is to set up tools such as Splashtop SOS or LogmeinRescue to allow IT support team to remotely access employees’ home machines to assist in setting up remote access. In the current issue the CounterCraft company that is a pioneering provider of cyber deception and counterintelligence products to detect targeted attacks is presented. CounterCraft’s solution deploys deception-based campaigns and offers deep monitoring and complex response actions. is used by government's law enforcement agencies. Some useful information about the problems of secure remote work is given by providing free e-books and free cybersecurity toolkits that will help to protect and manage remote workers. Remote workforce security tips and best practices from 8 security pros can be found too. Finally, some additional news and security tips about remote working at home is given. A list of links to Bulgarian, UK and international cyber-related institutions are also presented. Issue Editors: Prof. Daniela Borissova, DSc and on This Issue: Challenges related to the protection of employees' home networks and how smart devices changing the threat pattern associated with data sharing Remote work is not a new challenge. Many companies were already embracing remote work to give employees a better balance between work and life, along this contributes to reduce the carbon footprint and drive corporate efficiencies. [SEP] Justify:

Output: The text contrasts the claim by discussing the challenges associated with data sharing by smart devices. It mentions that home networks and smart devices are changing the threat pattern related to data sharing, which implies that there are risks associated with the use of smart home devices and suggests that personal data could potentially be shared or compromised. This directly contradicts the claim that no personal data is shared by these devices.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Do, D. T. , Yang, MR. , Lam, L. H. T. et al. Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. 12, 13412 (2022). Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences. Research Square; 2022. U-Segmentation and Characterization of Gliomas. Cancers. 2022; 14(18):4457. Chen, S. , Xu, Y. , Ye, M. , Li, Y. , Sun, Y. , Liang, J. , Lu, J. , Wang, Z. , Zhu, Z. , Zhang, X. , & Zhang, B. (2022). in Learning with Radiomics. Journal of clinical medicine, 11(12), 3445. inversion, sprint, Spyridon Bakas, Ujjwal. (2021). RSNA-. Kaggle. Martino, L. , Míguez, J. A generalization of the adaptive rejection sampling algorithm. 21, 633–647 (2011). Villa-Blanco, C. , Bielza, C. & Larrañaga, P. Feature subset selection for data and feature streams: a review. Rev 56 (Suppl 1), 1011–1062 (2023). Singh, S. (2022, November 23). Top methods to evaluate the performance of deep learning models. Labellerr. D. Chicco, V. Starovoitov and G. Jurman, "of the Coefficient (MCC) Over the Ratio (DOR) in Assessment," in IEEE Access, vol. 9, 2021. DOI: 10. 1109/. 2021. 3068614 Additional DeclarationsNo competing interests reported. :PostedVersion 1posted You are reading this latest preprint versionlets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various methodologies in radiomics and machine learning related to glioblastoma and does not mention anything about predicting emotions based on brain scans or advanced algorithms in that context.


Input: Task: corroborate the claim using the given text. Claim: Blockchain technology underpins most cryptocurrencies, ensuring transparency. [SEP] Text: Companies should adopt a proactive approach to risk management and stay abreast of developments to ensure the successful and secure implementation of blockchain solutions. Advertisement 4. ConclusionsBlockchain, at its core, operates as a decentralized and tamper-resistant digital ledger, offering a transparent and secure way to record and verify transactions. While it initially gained prominence through its association with cryptocurrencies like Bitcoin, the technology’s potential extends far beyond the realm of digital money. In the modern financial industry, blockchain’s distributed ledger concept is particularly promising. Traditional financial systems often rely on centralized databases maintained by trusted intermediaries, introducing vulnerabilities to fraud, errors, and data manipulation. Blockchain addresses these challenges by distributing the ledger across a network of nodes, ensuring consensus and transparency. This decentralized nature reduces the dependence on a central authority, fostering trust and integrity in financial transactions. However, blockchain technology is recognized as a foundational element in the financial sector, offering promising prospects for economic development, financial innovation, and internet advancement. As the financial industry undergoes a digital transformation, successful adoption of blockchain technology becomes imperative. Current research delves into the mediating role of blockchain adoption in the relationship between digital business strategy and process innovation, as well as financial performance. Furthermore, new findings investigate how information technology alignment plays a moderating role in this scenario. The investigation seeks to unveil the nuanced dynamics and interconnections among these crucial components, with the overarching goal of utilizing blockchain for strategic and operational improvements within the financial sector [9]. Moreover, Blockchain technology significantly impacts the financial sector by enhancing security through decentralization and cryptography, leading to efficient processes and reduced costs. Its transparency and traceability improve risk management, support cross-border transactions, and facilitate innovations such as decentralized finance (DeFi) and tokenization. Blockchain ensures regulatory compliance, strengthens data security, and prevents fraud, fostering financial inclusion and revolutionizing traditional financial products and services. Beyond finance, various industries are exploring blockchain applications. Sectors such as supply chain management, healthcare, real estate, and even governance are considering how blockchain can enhance efficiency, security, and transparency. For instance, in supply chain management, blockchain can enable real-time tracking of products from manufacturing to delivery, reducing fraud and ensuring the authenticity of goods. However, the adoption of blockchain technology comes with its share of challenges and risks. [SEP] Justify:

Output: The text corroborates the claim by explaining that blockchain operates as a decentralized and tamper-resistant digital ledger, which is a fundamental aspect of ensuring transparency in transactions. It specifically mentions the association of blockchain with cryptocurrencies like Bitcoin, emphasizing its role in fostering trust and integrity in financial transactions. Furthermore, the text highlights how blockchain enhances transparency and traceability while improving risk management, supporting the notion that blockchain is essential for transparency in cryptocurrencies.


Input: Task: pass the claim using the given text. Claim: Urban agriculture can support biodiversity by creating green spaces in urban areas. [SEP] Text: Possible effects of rainfall in urban areasFloodsTemporary surface cover with water related to climatic events (extreme rainfalls)represents a flow that exceeds the system’s maximum capacity and is discharged in the receiving waters. The overflow occurs at pump stations or in devices before the WWTP (Plant)Stormwater runoffRunoff produced by rainfall in impervious areas, collected in separate sewer systems and discharged in receiving watersTable 1. Results of rainfall events in urban areas. Reducing runoff in urban areas contributes to reduce the impacts of stormwater runoff on receiving water quality, and minimize the occurrence of the overflow of combined or separate systems. 1. 6 Roofs landscape in urban areasWe based our evaluation of the residential building stock in São Miguel island, namely in and Lagoa urban areas, on the (RBSS) developed by the University of the Azores in the framework of the Project, a project which associated the of Technology and several Universities in Portugal [14]. This RBSS was applied to a sample of 500 households in São Miguel, the largest and most populated island of the Azores. That database shows that 77. 3% of residential buildings in municipality and Lagoa municipality (corresponding to 295 buildings) were located in urban and periurban areas. From that sample of residential building stock, some statistical results are presented at Table 2. ParameterPercentage of buildings (%)Horizontal rooftops3. 1Elevated wind exposure24. 5Hight between 3 to 9 m78. 5Location at less than 50 m altitude46. 5Distance to ocean less than 1000 m56. 6Table 2. Characterization of residential buildings in and Lagoa urban and periurban areas [14]. Lately, with air space liberalization, there has been a major increase in tourism [15], enhancing the rehabilitation/transformation of the existing building stock and a boom of new constructions. This growth is confirmed by the registrations of new building constructions [1], between 2011 and 2021 in peri-urban areas of Ponta Delgada, like Fajã de Baixo (4. 42%) and São Roque (3. 86%), and much higher in Lagoa, where the increase of building stock varied between 11. 93% (Cabouco) and 4. 31% (Santa Cruz). Advertisement 2. Design of the experimental green rooftop pilot study2. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses the effects of rainfall and stormwater runoff in urban areas, as well as residential building stock statistics in urban and peri-urban regions. It does not mention urban agriculture, green spaces, or biodiversity, hence it does not support, contrast, or relate to the claim.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Abstract Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain. claim, responsible artificial intelligence, machine learning IntroductionA sizeable amount of healthcare spending is allocated to processing insurance claims, making the healthcare sector in the USA one of the world’s largest and most complex enterprises. National health expenditure (NHE) grew 4. 6% to $3. 6 trillion in 2018, accounting for 17. 7% of the gross domestic product (GDP) [1]. Medicare spending grew 6. 4% to $750. 2 billion, Medicaid expenditure grew 3. 0% to $597. 4 billion, private health insurance spending grew 5. 8% to $1. 243 trillion, and out-of-pocket spending grew 2. 8% to $375. [SEP] Justify:

Output: The text discusses the use of machine learning algorithms to predict health insurance claims, but it does not address the prediction of emotions based on brain scans. Therefore, it is irrelevant to the claim.


Input: Task: pass the claim using the given text. Claim: Bitcoin has a fixed supply cap of 21 million coins. [SEP] Text: The predicted translational start sites for the antidote (ATG in exon 1) and poison (ATG in exon 2) proteins are indicated, as is the FLEX transcriptional regulatory motif (Supplementary file 1b-d). (B) Long-read RNA sequencing of mRNAs from meiotic S. octosporus cells revealed two main transcript isoforms of the wtf25 gene, presumably encoding an antidote and a poison protein, respectively. cDNA reads obtained using the Technologies (ONT) platform are shown in pink. Blue lines indicate sequences missing in the reads due to splicing. The diagram at the top depicts the two main transcript isoforms. The 3' transcript ends shown in the diagram correspond to the major transcript end revealed by cDNA reads. Figure 2—source data 1 wtf genes share similar exon and intron sizes. Table of the average size (in base pairs) of the exons and introns of intact wtf genes in all Schizosaccharomyces species. For S. chizosaccharomyces pombe, only the genes containing 5 exons were considered. Download elife-81149-fig2-data1-v1. xlsx We next looked for similarities between promoters controlling the transcription of the S. pombe wtf genes and the potential promoters of the candidate wtf genes in other species. The promoters of the S. pombe wtf4 gene are representative of the promoters of wtf drivers in S. pombe (Nuckolls et al. , 2021). The Wtf4antidote protein is encoded on exons 1–6, with the promoter found upstream of exon 1. We found no shared homology between the S. pombe wtfantidote promoter sequences and sequences upstream of exon 1 in the candidate wtf genes found in the other species. The Wtf4poison protein is encoded on exons 2–6, and the promoter is found within what is intron 1 of the wtf4antidote transcript. The S. pombe wtf4 poison promoter contains a cis-regulatory FLEX motif that is bound by the Mei4 master meiotic transcription factor and is essential for expression of the Wtf4poison protein (Nuckolls et al. , 2021). [SEP] Justify:

Output: The text is irrelevant to the claim regarding Bitcoin's supply cap. It discusses genomic research related to the wtf genes in Schizosaccharomyces species, which has no connection to Bitcoin or its supply cap. Therefore, it does not corroborate or contrast the claim in any way.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: KeywordsInformal food systemsUrban agricultureFood securityFood resilience IntroductionSince the beginning of the twenty-first century, urban agriculture (UA) in sub-cities has been an expanding feature of the urban landscape and economy, mainly because of increasing food insecurity, poverty, and unemployment in many parts of the continent. Informal food enterprises, including street food, have evolved to be important sources of urban employment and food. Previous food studies in cities that dominate their national urban systems, such as Harare (Drakakis-Smith et al. , 1995; Mbiba, 1995), Lusaka (Hampwaye et al. , 2007), Kampala (Maxwell et al. , 1998), Maputo (Paganini et al. , 2018), and (Battersby & Marshak, 2013), have provided interesting insights into the drivers of UA and the spatial extent of the activity in several national cities across sub-Saharan Africa. Studies that have been done in the past decade have shed light on food production issues in secondary cities and towns. However, most of the studies have focused on single cities and have not generated comparative cross-country discussions on UA in different size and function urban centres. In recent years, secondary cities in Africa have been receiving increased attention, which is largely attributable to their vast numbers (about 7,500 across the continent) and their rapid growth since the new millennium (Tacoli & Agergaard, 2017). Secondary cities and towns are closely connected to the apex and usually primate cities (their main source of consumer and capital goods) and to their rural hinterlands (their primary source of cultural foods and various environmental resources, such as wild fruits and edible insects). of the (FAO) defines UA as any agricultural activity that entails the raising, growing, or processing (and distribution) of agricultural produce in cities and towns (FAO, 2001). In sub-Saharan Africa, UA takes place across a whole range of spaces, including backyard home gardens (especially in middle-income suburbs), community gardens (at sites such as school grounds, church premises, and community centres) and open space cultivation on vacant lots and road verges. UA in African cities has generated considerable debate regarding the impact of its contribution to household food and nutrition security (Battersby & Haysom, 2016; Binns & Lynch, 1998; Crush et al. [SEP] Justify:

Output: The text contrasts the claim by highlighting that urban agriculture (UA) is primarily driven by rising food insecurity and does not comprehensively address food production issues across multiple urban contexts. It mentions that urban agriculture is an important aspect of informal food systems, but does not support the notion that it can meet food demands on its own. The mention of previous studies focusing on the contributions of UA in various cities implies that there are additional factors and systems involved in meeting food demands.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 6 billion (these are 21%, 16%, 34%, and 10% of total NHE) in 2018 [1]. According to the 2021 national health statistics report, 28. 1 million individuals, or 8. 6% of the total population of the USA, lack health insurance coverage, 65. 4% have private health insurance, 56. 9% are employer-based, and 7. 2% have directly purchased coverage for the population under age 65 [2]. Among this age group, nearly two of every five children and one of every five adults relied on public health coverage through Medicaid and children’s health insurance programs. These estimates are characterized based on specific sociodemographic attributes, such as age, gender, race, Hispanic origin, family income, education, employment situation, and marital status [2]. A study explored that healthcare access is a fundamental right for every American citizen [3]. Unfortunately, the USA is confronted with a substantial and expanding segment of its population lacking insurance coverage. After hitting a low point of 28. 7 million individuals in 2016, the projected trajectory indicates a rise in the number of uninsured individuals to 37. 2 million by 2028 [3]. This concerning trend coincides with a period in which an increasing wealth of research establishes a strong connection between having insurance coverage and notable enhancements in financial stability, overall well-being, and lifespan [3]. Health insurance claims processing is crucial in this rapidly changing environment, as it holds the potential to revolutionize the healthcare landscape by providing prompt and accurate reimbursements and optimizing insurance provider cost management and risk assessment. The processing of health insurance claims entails looking over and verifying medical records, billing data, and payment authorization. The insurance market in the USA provides a vital safety net for both individuals and corporations. Insurance companies employ data and predictive modeling to measure risk and establish premiums. However, the insurance sector’s adoption of responsible artificial intelligence (AI) ensures that forecasting models are impartial, fair, and open. In the insurance sector, predicting insurance claims is essential because it aids insurers in calculating the likelihood of an event occurring and the potential cost. The employment of AI in this process has prompted questions about bias and discrimination, particularly against specific demographic groups. Developing and deploying prediction models can be guided by responsible AI principles to alleviate all the concerns. This includes ensuring the model is transparent, understandable, constantly monitored, and updated to prevent bias and discrimination. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses health insurance coverage statistics, the healthcare system in the USA, and the role of AI in insurance claims processing, without mentioning advanced algorithms or their application to predicting emotions based on brain scans.


Input: Task: pass the claim using the given text. Claim: Bitcoin has a fixed supply cap of 21 million coins. [SEP] Text: AES is part of a consortium of solar companies trying to incentivize a more resilient supply chain in the US. The consortium is promising to spend more than $6 billion on solar panels, as long as suppliers set up US manufacturing operations. “That’s what gives them certainty,” Moreno says. “They can build, and the demand will be there. ” To encourage production outside China and avoid supply chain risks, IEA analyst Bahar says, governments also need to offer solar manufacturers support. He says they should use tax policies or incentive schemes to reduce the start-up costs for polysilicon manufacturers and ensure that they have access to cheap electricity, just as China has done. In August, US president signed the Act into law. It offers a direct tax credit for the production of solar-grade polysilicon and also offers incentives for other parts of the supply chain, like ingot and wafer manufacturing. Related: U. S. In an investor call the day after the signing, REC Silicon’s May said the tax credits were a big part of the company’s decision to restart operations at its plant in Washington. The company expects to receive $48 million in credits annually once the facility is operating at full capacity. Korean company Hanwha Solutions, which made a major investment in REC Silicon earlier this year, is scouting out sites in Texas, South Carolina, and Georgia for a facility to produce ingots, wafers, and solar panels, according to documents filed with the Texas comptroller’s office. If the project moves forward, the company says, it would begin operating in 2025. SPI Energy hopes to set up wafer production in the US by 2023, and hopes to be producing wafers in upstate by 2024. Credit: produces polysilicon using the fluidized-bed reactor process, which consumes less energy than the more typical Siemens process. Building wafer manufacturing in the US would have a domino effect, Bernreuter says, and likely complete a solar supply chain outside China. “If you solve that step of the value chain, I think a lot of the other steps could follow relatively easily,” he says. The US could also avoid silicon-based solar panels altogether by using another technology: thin-film cadmium telluride, which makes up about 5% of the global solar market. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses solar companies, supply chain issues, and manufacturing without any mention of Bitcoin or its supply cap. Therefore, it does not corroborate or contrast the claim.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: , 2011, 2018; Drescher, 1996; Ferreira et al. , 2018; Hampwaye, 2013; Riley et al. , 2018). Its contribution to urban employment and incomes has also been hotly contested, largely because of the non-availability of reliable data on the activity, which takes place behind high security walls (in the case of urban home gardens in many Zimbabwean urban centres) and well beyond the watchful gaze of officials. The benefits of UA have been highlighted in the literature and they include the improvement of environmental quality (through green spaces), improvement of household food security, and the production of cost-effective food supplies in urban spaces (Modibedi et al. , 2021; Simatele & Binns, 2008; Simatele et al. , 2012). Several scholars have also acknowledged the importance of UA in Africa and its ubiquity in post-independence African cities. For example, Thabo Modibedi, Mosima Masekoameng, and (2021) have argued that urban community gardens have contributed to the mitigation of food insecurity in low-income urban areas in the Gauteng Province, South Africa’s commercial and industrial hub. However, studies done by the Network (AFSUN) across 11 cities in reveal that the alleged contribution of UA to the nutritional status of urban households is exaggerated, if one considers the low levels of participation in many low-income areas (Crush et al. , 2018). Similarly, studies by (2016), Bruce Frayne, Cameron McCordic, and (2014), and (2015) support the view that the contribution of UA in to the nutritional status of urban households is exaggerated. This 1. 3 billion by 2050 and a significant portion of this population will be residing in secondary cities with less than 500,000 residents. Many of these secondary cities are experiencing rapid growth that is attributable to rural–urban migration. These secondary cities experienced considerable disruptions to their local food systems following various government-enforced economic lockdowns implemented in 2020 and 2021 to slow the spread of the COVID-19 virus (FAO, 2020). The disruption to food systems by COVID-19 highlights the need to reconnect local production and consumption. One way to achieve this might be through deliberate promotion of commercial agricultural activities in the peri-urban zones of these cities so as to make them better positioned to withstand future shocks. [SEP] Justify:

Output: The text contrasts the claim by stating that studies have shown the alleged contribution of urban agriculture (UA) to the nutritional status of urban households is exaggerated. It mentions low levels of participation in low-income areas and emphasizes the need for reconnection of local production and consumption, suggesting that relying on UA alone is insufficient to meet the food demands of major cities.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: This study explores how the potential application of responsible AI affects claims processing accuracy and identifies the most useful model for forecasting a customer’s insurance claim process. Various methods have been explored to address concerns about AI algorithm interpretability [4]. Recent studies have created metrics for the relevance of specific covariates based on their contribution to model prediction accuracy [4]. Furthermore, it investigates the variables that can be investigated to forecast insurance claims with greater precision and address any potential ethical issues by using predictive models for processing insurance claims. To prevent prejudice and ensure that clients receive the coverage they require, it is crucial to ensure that AI systems used in insurance are developed and implemented transparently, equitably, and ethically. The use of AI in the insurance sector has raised moral questions regarding transparency, fairness, and bias, particularly in the prediction of insurance claims. Consequently, there is an increasing focus on employing responsible AI within this sector. Ensuring the responsible development and implementation of AI systems involves transparent and ethical consideration of their potential impacts on individuals and society. This policy must be followed to protect consumers from unfair treatment and prevent discrimination against specific categories of individuals. In the insurance sector, predicting insurance claims is essential because it aids insurers in calculating the likelihood of an event occurring and the potential cost. The use of AI in this process has prompted questions about bias and discrimination, particularly against specific demographic groups. Responsible AI principles can direct the creation and use of prediction models to allay all worries. The data used to train the model must also be varied and representative of the community. A crucial area of concentration for the insurance business is the use of responsible AI in the forecasting of insurance claims in the USA. Moreover, the use of responsible AI for forecasting insurance claims can add to the body of knowledge regarding AI ethics and governance. AI technologies must be used responsibly, ethically, and within legal and regulatory frameworks as they become more common and significant. This study seeks to answer the following questions:(1)What model can be used to predict an efficient insurance claim process for a customer? (2)Which variable could be analyzed to anticipate insurance claims more accurately? (3)What potential ethical considerations are associated with using such predictive models? To identify best practices and prospective areas for development, this study also analyzes the existing literature on AI and insurance claim processing. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the use of AI in predicting insurance claims rather than predicting emotions from brain scans. The focus is on responsible AI in insurance processes, with no mention of emotional prediction or the application of algorithms in that context.


Input: Task: pass the claim using the given text. Claim: CRISPR technology allows precise editing of specific genes. [SEP] Text: We further speculate that the discrepancy between the genomic PCR and immunofluorescence could result from a more complete co-transfection using electroporation and a bias towards easier detection of larger PCR products (i. e. , the KI and WT fragments) using ethidium bromide integration for detection. We have now included these data in the manuscript as Figure 4—figure supplement 3 and discussed this in the context of the GluA2 KO seen with TKIT (Results). Also see the Discussion. We also added a description of this methodology to the Materials and methods section of the manuscript. 2) Recent reports suggest that AAV vector genomes can integrate at DSBs created by Cas9 (PMIDs: 33087932, 31570731), and Cas9 can often create an opportunity for unintended insertions. Although PCR of genomic DNA (shown in Figure 4—figure supplement 2) detected both WT and KI alleles, it is not clear how often insertion of other sequences (with variable length) occurs, and no data at the level of DNA sequence is provided following AAV delivery methods. The fidelity of clean donor insertion could be examined by Sanger sequencing of genomic DNA clones to more completely define the results of insertion and genomic sequence changes around cut sites. This would also help to identify potential issues that may affect non-coding sequences (which, at the locations targeted here, are likely to be important regulatory elements). We are grateful to the reviewers for raising this important point. We agree that this is a necessary consideration when coupling AAVs withCas9 methods for genome editing (which is being widely adopted in the field). In fact, we now also speculate that 5’UTR integration of fragments of AAV could explain the lower efficiency in vivo with AAVs compared to in vitro with transfections. We have updated the text in the Results section to account for this possibility and include the recommended 2. The hippocampus will then be dissected, and genomic DNA amplify using a high-fidelity polymerase and a very long extension time (to account for variable sized insertions) flanking the KI sites. The PCR products will be sent for deep sequencing (as in Hanlon et al. , 2019). This will enable us to calculate the percentage of reads that have AAV insertions. [SEP] Justify:

Output: The text discusses various aspects of gene editing and methodologies related to the use of AAV vectors and Cas9, but it does not directly address or support the claim regarding CRISPR technology's ability to allow precise editing of specific genes. Instead, it mentions concerns about unintended insertions and the fidelity of donor insertions, which do not corroborate or contrast with the claim but rather explore technical details related to the editing process.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: The levels of urban planning and management vary considerably from city to city, as do the urban planning policies that aim to control UA. The limited levels of urban land use planning are generally a reflection of the underdeveloped institutional capacities to plan and enforce regulations. According to (2017), a majority of local authorities that manage small urban centres in sub-are constrained by having insufficient capacity or funding to fulfill their mandates. Also, urban planning in many secondary urban centres has largely been top-down, with limited input from stakeholders. Urban transformations in secondary cities in sub-have generated renewed interest in food systems resilience in smaller towns. Two main questions are pertinent. First, will the 7,500 secondary cities that account for 48% of Africa’s urban centres be able to achieve Goal 2 on hunger eradication by 2030? Second, is UA making a meaningful contribution to the urban food system, especially food production? While urban development in sub-is characterized by rapid population growth and increasing urban food insecurity, secondary urban centres such as Nakuru (Kenya), Ndola (Zambia), and Karoi (Zimbabwe) have recorded high growth rates over the past 20 years, and this has had consequential effects on urban food security livelihoods. Nakuru is a secondary city whose population growth during the past two decades has been in excess of job creation capacity. This has given rise to urban informality characterized by numerous street trading activities and informal settlements. Between 1969 and 2020, the population of Nakuru increased from 47,000 to 383,000. The high rate of population growth has been accompanied by rising poverty and an uptake of UA (Foeken & Owuor, 2008; Owuor, 2006). In Nakuru, UA activities in the peri-urban zone have contributed to the city’s local food system, as some of the produce finds its way to local vegetable markets (Foeken & Owuor, 2008; Owuor, 2006). However, it is unlikely that the contribution of UA to local food production can be maintained because the urban development projects that have been implemented in recent years in the peri-urban zone (Willkomm et al. , 2021) and in open spaces will reduce the space available for UA. Ndola is an intermediate city with a population of about 500,000, including a large labour force that is employed in copper mining and allied industries. [SEP] Justify:

Output: The text contrasts the claim by detailing the limitations of urban agriculture (UA) in urban planning and food production. It highlights the underdeveloped institutional capacities for urban planning, insufficient capacity or funding for local authorities, and the challenges posed by rapid population growth and urban food insecurity. Specifically, it states that while UA contributes to local food systems in cities like Nakuru, it is unlikely that this contribution can be maintained due to urban development projects reducing space available for UA, thus indicating that UA alone cannot fully meet the food demands.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: In recent years, several businesses have started using AI to automate jobs that people are typically hired to complete, such as detecting fraudulent movements, choosing resumes, processing credit-related requests, and releasing those people for high-level duties [5]. According to a survey of 2,360 business executives about the use of AI, more than 62% of executives said that AI solutions increased their revenues, decreased their costs, and enhanced customer satisfaction [6]. AI has proven its value in different business sectors by quickly establishing automated environments that are controlled and digitally upgraded for maximum efficiency in 2017 [7]. The use of AI in Tanzania’s healthcare sector includes applications for disease prediction and diagnosis, vaccine stock optimization, and health supply chain management [8]. AI can assist in the methods mentioned above to improve customer satisfaction and revenues and cut down on fraud, inefficient time use, and operational complexity [9]. One of the important areas across Europe where AI can address many issues with the health system is the health sector [10]. Several problems prevent medical AI from being used properly and effectively. These confrontations include data privacy, intellectual property rights, accountability, openness, cybersecurity, accuracy, performance, bias, and discrimination. Thus, it is advised that strategic decisions should be made when putting AI-based innovations into practice for companies to make sure that i) they are responsible, ii) the challenges are properly addressed, and iii) there is a balance between opposing interests and values [10, 11]. Besides the hazards, the growing application of AI exacerbates intrinsic problems with trust and accountability. Enterprises must be aware of the difficulties and dangers associated with AI and take these into full consideration when presenting suggested plans to effectively address these issues [11]. Adaptive boosting (AdaBoost), a whitebox algorithm, outperforms all additional models in terms of performance, aids in lowering operating costs for providers, improves the speed and accuracy of the insurance claim process and allows patients to concentrate on their recovery rather than navigating the insurance claim appeals process [12]. Because of the unstable nature of intelligent applications, the theoretical paradigm for the growth of responsible AI was founded on perceived risk theory. Digital healthcare AI risks are inversely correlated with responsible AI [13]. There should be a complete framework for responsible AI that businesses can use to emphasize and address important issues when developing and implementing responsible AI applications. [SEP] Justify:

Output: The text is irrelevant to the claim since it discusses the use of AI in business and healthcare applications but does not mention anything about predicting emotions based on brain scans. The focus is on AI's role in automating jobs, improving customer satisfaction, and tackling issues within the healthcare sector, without addressing emotion prediction.


Input: Task: corroborate the claim using the given text. Claim: CRISPR technology allows precise editing of specific genes. [SEP] Text: An efficient and specific -Cas9 genome editing system targeting soybean phytoene desaturase genes access Published: 15 February 2022 An efficient and specific -Cas9 genome editing system targeting soybean phytoene desaturase genes Mimmie Lu1 & Lining Tian1 BMC Biotechnology 82. Transformed plants for all five constructs were recovered. Dwarf and albino phenotypes were observed in transgenic plants harboring the constructs targeting both PDS genes. Gene editing at the desired loci was detected in the majority of T0 transgenic plants, with 75–100% mutation efficiencies. Indel frequencies varied widely among plants (3–100%), with those exhibiting visible mutant phenotypes showing higher frequencies (27–100%). Deletion was the predominant mutation type, although 1-nucleotide insertion was also observed. Constructs designed to target only one PDS gene did not induce mutation in the other homologous counterpart; and no mutation at several potential off-target loci was detected, indicating high editing specificity. Modifications in both PDS genes were transmitted to T1 progenies, including plants that were negative for transgene detection. Strong mutant phenotypes were also observed in T1 plants. ConclusionsUsing simple constructs containing one guide RNA, we demonstrated efficient and specificCas9-mediated mutagenesis in stably transformed soybean plants, and showed that the mutations could be inherited in progenies, even in plants that lost transgenes through segregation. The established system can be employed to edit other genes for soybean trait improvement. BackgroundGenome editing using the clustered regularly interspaced short palindromic repeats/-associated 9 (/Cas9) system has emerged as a versatile tool to modify genes at precise locations. It was initially discovered as bacterial immune system against viruses and other foreign nucleic acids such as plasmids [1,2,3,4,5]. refers to direct repeats in bacterial genome separated by short stretches of variable sequences called spacers derived from invading genetic material [1, 4, 6]. Cas genes are often located adjacent to which encode proteins with RuvC-like and HNH-like nuclease domains [5, 7,8,9]. Transcription of locus generates a non-coding precursor RNA cleaved into short (crRNAs), which direct the Cas proteins to cleave foreign nucleic acids containing complementary target sequences [2]. [SEP] Justify:

Output: The text corroborates the claim by detailing how the Cas9 genome editing system enables specific targeting and editing of genes in soybean plants. It emphasizes the precision of the method by reporting that mutations were detected at desired loci with high mutation efficiencies and that constructs targeting only one gene did not affect the other homologous gene, indicating a high degree of specificity in gene editing.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: The city experienced a long period of steady growth from the 1960s to the early 1990s. However, following the decline of copper mining and de-urbanization in the copper belt during the 1990s, the rate of growth has declined. The location of Ndola in the country’s copper mining region has resulted in the growth of close ties with the global economy and also with a huge rural hinterland that has provided the mines with cheap semi-skilled labour. Karoi, on the other hand, is a small agricultural town of fewer than 35,000 people, situated in the heartland of one of Zimbabwe’s most productive farming regions. However, with the repeal of influx control measures following the country’s attainment of majority rule in 1980, Karoi experienced relatively rapid growth during the 1980s and 1990s. This 7. 1 highlights several common elements of the UA experiences of Karoi, Nakuru, and Ndola, despite their considerable differences in terms of size, economic base, and UA policies. The table presents key aspects of UA in these urban centres, captured in the following broad themes: Theme 1 highlights the main factors that motivate households to engage in UA; Theme 2 presents the socio-demographic profiles of the urban farmers in terms of age, gender, educational attainment, and income class; Theme 3 highlights the dominant UA activities; Theme 4 identifies the bottlenecks that are blocking the uptake of UA; and Theme 5 provides an overview of the policy environments in the three urban centres and their effects on UA. Table 7. 1 Key aspects of urban agriculture in Karoi (Zimbabwe), Nakuru (Kenya), and Ndola (Zambia)Full size tableTheme 1: Drivers of UAUA is driven by a variety of factors, including urban food insecurity, urban poverty, entrepreneurial aspirations, desire for ethnic foods, and shocks to the urban food systems that are caused by economic, environmental (e. g. , drought), and health crises (e. g. , government-induced COVID-19 lockdowns). Table 7. 1 highlights the main reasons why both poor households and relatively well-resourced commercial farmers have engaged in UA in the three secondary urban centres. [SEP] Justify:

Output: The text contrasts the claim by highlighting the complexity of urban agriculture (UA) and its inability to fully address the food demands of urban centers. It mentions various factors that motivate households to engage in UA, such as urban food insecurity and economic shocks, but it also suggests that there are bottlenecks blocking the uptake of UA. These complexities imply that while UA is an important aspect of urban food systems, it is not sufficient on its own to meet the full food demands of major cities.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Governance provides an ongoing foundation for all other aspects and assists businesses in creating AI that complies with the relevant regulations and upholds ethical standards [14]. The operation of the Apriori algorithm consists of two steps: First, obtain frequent item sets of the largest possible size, and then, use these frequent item sets to generate rules by locating all their subsets [14]. Both supervised and unsupervised machine learning (ML) algorithms were used, including support vector machine (SVM), logistic regression, naive Bayes, random forest (RF) classifiers, deep neural networks, and AdaBoost. They examined the efficacy of every algorithm for detecting blockchain fraud and discovered that RF, AdaBoost, and SVM generated effective outcomes [15]. The aftereffects of the different processes reduced the number of claims filed as a text message was sent to the insured individual that full or a portion of a claim may be denied. Furthermore, it might impose conditions on the claimant as the decision is being made, which could prolong the process [16]. The paper’s primary goal is to use the Apriori algorithm to find similarities between medical bills and purchasing bills. This method searches a database of frequently occurring item sets to identify item sets whose occurrences exceed a specified threshold [17]. The main objective while building an AI solution is to identify the easiest model that performs the best. Using ML to solve problems in trading and investment management has made things more useful and opened new options for the economy. This is possible because computers are getting faster, data storage costs are going down, and big data is ready to use [18, 19]. A supervised learning rule first completes a foundational task using sample data and then attempts to build a temporary performance, leading to the plotting of new input vectors. In several application areas, supervised learning algorithms are used. A comparable goal is for the supervised learning rule to cut back superbly from the knowledge to the contained objects in the best possible setting, helping the rule to appropriately index the class labels for near occurrences [20–22]. It is necessary to continuously test various AI algorithms because the performance of an AI model varies with the core data structures. AI systems’ accuracy, simplicity, and inter-portability can all be traded off. To choose the finest-performing AI algorithm with the least amount of complexity and the greatest degree of interpretability, it is crucial to investigate several AI algorithms. [SEP] Justify:

Output: The text does not address the claim regarding advanced algorithms predicting emotions from brain scans. Instead, it discusses various aspects of AI governance, machine learning algorithms, and their applications in fraud detection and economic benefits, none of which pertain to emotion prediction based on brain scans.


Input: Task: corroborate the claim using the given text. Claim: CRISPR technology allows precise editing of specific genes. [SEP] Text: system from Streptococcus pyogenes is among the best characterized. It is consisted of nuclease Cas9, crRNA, and an auxiliary trans-activating crRNA (tracrRNA) required for processing of crRNA into functioning units [7, 10]. In the engineeredCas9 system, crRNA and tracrRNA are fused into a single guide RNA (sgRNA) [7, 11]. Cas9 is guided to specific genomic locus by sgRNA containing a 20-nucleotide (nt) target sequence, which immediately precedes the protospacer adjacent motif (PAM), with the sequence 5’-NGG-3’ in the system derived from Streptococcus pyogenes [7, 11,12,13]. At the target site, Cas9 RuvC and HNH-like nuclease domains each cleave one DNA strand, creating a double stranded break (DSB) at 3 base pairs (bp) upstream of PAM [7, 9]. The cleaved genomic locus then activates DNA damage repair, either by nonhomologous end joining (NHEJ) pathway or homology-directed repair (HDR) [11,12,13]. In the absence of a repair template, the error prone NHEJ pathway is activated, creating insertion or deletion (indel) mutations. HDR pathway requires the presence of homologous DNA template surrounding the DSB, which can be delivered by a plasmid or single-stranded DNA oligos [12, 13]. Once mutations are induced at target loci, novel traits can be retained in transgene-free mutants through transient Cas9/sgRNA expression or Mendelian gene segregation. Genome editing usingCas9 has been employed in various plant species of commercial importance such as rice [13], wheat [13, 14], apple [15], tomato [16], grape [17, 18], melon [19], etc. This technology has become a promising tool for crop trait improvement. Soybean (Glycine max L. Merr. ) is a globally important crop that provides a rich source of protein and oil. Since year 2015, genome editing in soybean has been performed by several research groups, often using hairy root system [20,21,22,23,24,25,26,27,28,29]. [SEP] Justify:

Output: The text corroborates the claim as it describes the CRISPR system, particularly detailing how the Cas9 nuclease, guided by a single guide RNA (sgRNA), targets specific genomic loci for precise gene editing. It explains that this targeting is based on a 20-nucleotide target sequence that directs the Cas9 to create double-stranded breaks at specific locations in the DNA. Furthermore, after these breaks, it mentions the activation of DNA damage repair mechanisms, which can lead to precise editing outcomes, highlighting the technology's application in genome editing for various plant species, thereby supporting the claim about CRISPR's capability for precise gene editing.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: Lack of access to food, because it is either too expensive or unavailable for purchase, is one of the main reasons why many low-income households have resorted to UA under fairly challenging conditions (Foeken & Owuor, 2008; Gondo et al. , 2017; Voleníková & Opršal, 2016; Willkomm et al. , 2021). Urban food production is largely a response by the urban poor to inadequate, unreliable, and irregular access to food supplies (Foeken & Owuor, 2008; Modibedi et al. , 2021; Zezza & Tasciotti, 2010). The need to produce fresh and culturally preferable food has also compelled some middle-income households to grow the food they consume. The persistence of UA in African cities is attributed to factors such as expensive urban food, urban food deserts, high unemployment levels, and food rights initiatives. Theme 2: Profile of Urban FarmersTable 7. 1 shows that in Nakuru, Ndola, and Karoi, the major groups involved in UA are women, middle-aged residents (over 40 years old), people with regular incomes, and fairly educated urban dwellers with high school education (Foeken & Owuor, 2008; Gondo et al. , 2017; Voleníková & Opršal, 2016). The limited involvement of young people in the activity implies reduced opportunities for potential transfer of UA skills and knowledge to the younger generation. Regarding the role of women involved in UA, it is essential to think beyond the numbers and to consider women’s involvement in terms of their contribution to the gendering of UA (Hovorka & Lee-Smith, 2006; Hovorka et al. , 2009; Ishani, 2009). It is clear from all three urban centres that since women are actively engaged in UA activities they are contributing to urban food and nutrition security in ways that are often missed by urban food studies that focus on where households buy their food. However, further studies in Nakuru, Ndola, and Karoi could try to establish whether households that are engaged in UA are more food secure than households that do not participate in the activity. An earlier study by (2004) also argues for looking at UA through an alternative entrepreneurship prism. [SEP] Justify:

Output: The text contrasts the claim by highlighting the challenges that necessitate urban agriculture (UA), such as expensive food access and urban food deserts, indicating that UA is more of a response to food insecurity rather than a solution that can fully meet urban demands. It points out that urban agriculture is a reaction from low-income households facing inadequate food supplies, thus implying that urban agriculture alone may not be sufficient to meet the food demands of major cities.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The intended study uses six AI algorithms: two interpretable, two whiteboxes, and two blackboxes [23]. Materials and methodsNecessary data were gathered to construct ML models based on USA health insurance claims by people aged 18 to 60 in four different regions [24]. The data covers information about insurance claims, including age, gender, body mass index (BMI), blood pressure, diabetic status, number of children, smoking status, and region of the insured person. It must be cleaned up and sorted before the data is used to build ML models. Data cleaning eliminates erroneous data by extracting input and output features that contribute to effectively fitting the best model. A description of the features is given in Table 1. Data preparation is the vital process of refining and adapting data to make it well-suited for ML algorithms. The quality of this process significantly influences the model’s performance. It encompasses tasks such as data cleansing, exploratory data analysis (EDA), standardization, and reducing dimensionality. Data cleaning is the key step of the following retrieval, which involves identifying and eliminating erroneous, misleading, incomplete, and corrupt data. By using a mean substitution, a dummy value that treats the missing feature values as missing values can take their place. Table 1. Description of health insurance databaseNumberVariable nameExplanation1. AgeAge of primary beneficiary2. GenderGender of the beneficiary3. of the beneficiary (kg/m2) using the ratio of height to weight, ideally 18. 5 kg/m2 to 25 kg/m24. Blood pressureWhether the insured person has blood pressure (mmHg) or not5. DiabeticWhether the insured person is diabetic or not6. ChildrenNumber of children of the insured person7. SmokerWhether the insured person is a smoker or not8. RegionThe residential areas of the beneficiary in the USA are and Northwest USA9. ClaimAmount of the insurance claimDisplay full sizeEDA is applied to understand the data before applying the ML model. Data visualization with graphs reveals concealed connections between features that cannot be detected by simply viewing the dataset. The study used a heatmap to visualize the correlation between the pair of variables and to understand the significance of all the explanatory variables concerning the response variable in Figure 1. The heatmap shows a strong relationship between blood pressure and insurance claims, followed by BMI, which has an impact on insurance claims. Display full sizeFigure 1. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the construction and data preparation for machine learning models based on health insurance claims, rather than predicting emotions based on brain scans. It outlines data collection and analysis methods without any mention of emotions or related algorithms.


Input: Task: corroborate the claim using the given text. Claim: CRISPR technology allows precise editing of specific genes. [SEP] Text: To evaluate gene targeting efficiency and specificity, we created three constructs targeting each PDS gene specifically as well as two constructs targeting both genes simultaneously, using one guide RNA in each construct. Instead of using cotyledonary nodes derived from germinated seedlings, we performed Agrobacterium-mediated transformation of cv. Williams 82 using ‘half-seed’ explants dissected directly from overnight-imbibed seeds. Transformed plants for all constructs were recovered. Mutations at desired loci were induced in T0 plants with high specificity and efficiencies, and were transmitted to T1 progenies. Simultaneous targeting of both PDS genes resulted in visible mutant phenotypes in both T0 and T1 generations. Our efficient and specificCas9 genome editing system can be employed to modify other genes in soybean through whole plant transformation for agronomic trait improvement. ResultsSoybean is a palaeopolyploid organism which underwent genome duplication in ancient times, nearly 75% of its genes are in multiple copies [44]. The soybean genome contains two highly homologous PDS genes: Glyma. 11g253000 on chromosome 11 (hereafter named GmPDS11g) and Glyma. 18g003900 on chromosome 18 (hereafter named GmPDS18g). The two paralogues have 13 exons in each, sharing 96% identity in nucleotide coding sequence (Additional file 1, Figure S1), and 98% identity at amino acid level (Additional file 2, Figure S2). We created an array of genome editing constructs, including two constructs (GmPDS1 and GmPDS3) targeting GmPDS18g specifically, one construct (GmPDS7) targeting GmPDS11g specifically, and two constructs (GmPDS8 and GmPDS9) targeting both genes simultaneously at conserved regions (Table 1). We used -PLANT online tool [45] to select guide sequences targeting each gene specifically. Sequences targeting both PDS genes simultaneously were designed manually by selecting 20 nucleotides in the conserved regions immediately preceding PAM ‘-NGG-’, on exons in the upstream locations. The sequences were then used as queries for Tool (BLAST) in Phytozome [46] to check for specificity. [SEP] Justify:

Output: The text corroborates the claim by detailing the creation of genome editing constructs targeting specific genes in soybean using CRISPR technology. It describes how mutations at desired loci were induced in T0 plants with high specificity and efficiencies and that specific guide RNAs were used to target the genes. This supports the assertion that CRISPR technology enables precise editing of specific genes.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: The various studies conducted in the three secondary urban centres help to dispel the myth that people engaged in UA in Africa are predominantly poor and recent arrivals from rural areas who have run out of livelihood options. A study by David Mkwambisi, Evan Fraser, and (2011) on UA in Lilongwe and Blantyre (Malawi) also found that UA is not a preserve of the poor. The study also noted that urban food production in the two cities in Malawi is dominated by high-income households because, unlike low-income households, this group of urban farmers has the capacity to cultivate larger pieces of land and to purchase the required agricultural inputs, such as seeds, fertilizers, and pesticides. They also hire farm labour from low-income urban neighbourhoods. While this creates employment for a group of people who would have been unemployed, it also tends to create a group of urban farm labourers and to reproduce social inequalities. Urban farmers are commonly portrayed through a poverty lens as resource-poor urbanites who engage in undercapitalized micro-farming activities as a livelihood strategy, but the experiences of Ndola and Nakuru suggest that while poverty is the driving force for many low-income urban farmers, many households have engaged in the activity for entrepreneurial purposes. It is also worth noting that the most poverty-stricken households do not own spaces they can use for cultivation, and neither can they afford to rent. In fact, the poor tend to be transient and do not have the cash and social capital to enable them to access urban land for cultivation. In view of this, the open spaces in Karoi, Nakuru, and Ndola should not be perceived as sites of survivalist pursuits of the poor; instead they should be viewed as spaces of resistance to top-down urban planning approaches by both resource-rich and resource-poor households. Theme 3: Dominant ActivitiesCultivation of maize and leafy vegetables is practiced by urban farmers in the three secondary urban centres, with leafy vegetables generally grown in home gardens and maize in open spaces during the rain season. Urban farmers in Nakuru and Ndola with relatively large plots are able to keep chickens, but in Karoi the municipal bylaws make it illegal to keep livestock in urban areas. Theme 4: Urban FarmersUrban farmers in the three secondary urban centres face several common challenges, such as limited access to suitable land for cultivation, land tenure insecurity, and theft of produce before harvesting. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and challenges faced by urban farmers, such as limited access to suitable land for cultivation, land tenure insecurity, and the fact that the poorest households often do not engage in urban agriculture due to lack of resources. While it highlights some positive aspects of urban agriculture, it implies that it cannot fully meet the food demands of major cities due to these systemic issues and the economic disparities among urban farmers.


Input: Task: pass the claim using the given text. Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Heatmap of variable interactions in health insurance claimsDifferent graphs are also displayed in Figures 2 and 3 to observe unique values of the categorical variables and the distribution of explanatory variables respectively. The age distribution is even, with a slight increase among 20–30-year-olds. BMI is normally distributed, with a peak around 30–35. Blood pressure readings are right skewed, mostly between 80–100 mmHg (1 mmHg = 0. 13332 kPa) (Figure 3). The number of children per individual decreases as the number increases, with most having 0–2 children. Claim amounts are right-skewed, with most of the claims below $10,000, indicating a dataset that likely represents a demographic of young to middle-aged adults with a range of health-related characteristics and insurance claim amounts. Display full sizeFigure 2. Number of unique values of categorical variablesDisplay full sizeFigure 3. Frequency distribution of the numerical variables. A. Histogram of age; B. histogram of BMI; C. histogram of blood pressure; D. histogram of children; E. histogram of claimThen, one-hot encoding involves transforming categorical data into a format that can contribute to ML algorithms to increase prediction accuracy. ML commonly uses one-hot encoding to handle categorical input. This gives the idea of transforming all categorical values into numerical values. Categorical data can be more expressively represented by one-hot encoding. The need for it arises from the fact that many ML algorithms fail to operate and produce the desired results when given categorical input. Data is divided into portions for training and testing, with 80% of the data being used for training and 20% of the data for testing. The dataset contains 1,065 rows and 14 columns for training and the dataset contains 267 rows and 14 columns for testing. After the data preparation is done, the next step involves transitioning into model building. Here, the target variable has discrete values, and different regression models are used for future claim prediction. Therefore, the study used different performance measures for all models, using parametric and non-parametric regression models, such as decision tree (DT), SVM, RF regressor (RFR), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN) algorithm, and linear regression (LR). [SEP] Justify:

Output: The text does not address the claim regarding advanced algorithms predicting emotions based on brain scans. Instead, it focuses on health insurance claims and various regression models used for predicting claim amounts, without any mention of emotions or brain scans. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: CRISPR technology allows precise editing of specific genes. [SEP] Text: H GmPDS9 plantlet with variegated leaves (pointed by arrow)Full size imageMutations were detected in T0 GmPDS1, GmPDS3, and GmPDS7 plants, in the desired loci specificallyTo analyze gene editing, we sequenced PDS gene fragments encompassing the target sites in GmPDS1, GmPDS3, and GmPDS7 T0 plants verified for transformation. To examine the presence of basal mutation in the regions, we also sequenced regenerated wild type controls, as well as several GmPDS3 plants that were negative for transgene detection. In all cases, gene fragments amplified from wild type plants matched the reference sequences. Plants that were negative for transgene detection also showed no sequence change (Additional file 4: Table S2), indicating absence of basal mutation in the loci. Mutations at target sites were detected in all GmPDS1 and GmPDS3 transgenic plants, with 100% mutation efficiencies. Sequence changes were also detected in three GmPDS7 transformants, corresponding to 75% mutation efficiency (Table 2). Table 2 Summary ofCas9-induced mutations in T0 plantsFull size tableFor each transgenic plant, we sequenced both GmPDS11g and GmPDS18g regions. The target fragments were amplified from genomic DNA, cloned into plasmid pGateG [48], and transferred into E. coli. Plasmids were extracted from randomly selected E. coli colonies and sequenced. Of the two GmPDS1 plants, 6. 5% and 16% of sequenced clones had mutations in GmPDS18g, these numbers represent indel frequencies at the target site. Likewise, the five transgenic GmPDS3 plants had indel frequencies of 8%, 12%, 23%, 74% and 88% in GmPDS18g (Table 2, Additional file 4: Table S2). On the other hand, no sequence change was detected in the homologous counterparts in GmPDS11g, providing evidence of specific targeting. Indels in GmPDS11g were detected in three GmPDS7 transformants, among 9%, 14%, and 21% of sequenced clones (Table 2, Additional file 4: Table S2). [SEP] Justify:

Output: The text corroborates the claim by detailing specific instances of gene editing that were achieved through CRISPR technology. It mentions mutations detected in specific genes (GmPDS1, GmPDS3, GmPDS7) at targeted loci with high mutation efficiencies, indicating precise editing has taken place. The text also supports specific targeting by revealing that there were no sequence changes in homologous counterparts, further demonstrating the accuracy of CRISPR technology.


Input: Task: contrast the claim using the given text. Claim: Urban agriculture alone can fully meet the food demands of major cities. [SEP] Text: Projects’ and Frederic R. Siegel2 Part of the book series: SpringerBriefs in Geography (()) 1178 Accesses AbstractMany old and “modern” cities have been built on terrain that was previously vegetated and part of biologically diverse ecosystems. This was often on productive farmland that was not protected from encroachment as municipalities expanded and opened pristine areas to development. The encroachment continues as rural citizens and their families are attracted to urban centers for the way of life they seemed to offer. This starts with employment possibilities, education for workers children, and family health services as manufacturing/industrial operations and large and small businesses that serviced them spurs economic development. Agricultural projects are located mainly away from population centers, be they major metropolises or smaller cities. They provide food security for urban citizens through “brought into markets” in-ground crops, bush crops, and tree crops they cultivate, and through animal husbandry (e. g. , beef and dairy cattle, poultry, hogs, sheep). The last 6. 1 IntroductionMany old and “modern” cities have been built on terrain that was previously vegetated and part of biologically diverse ecosystems. This was often on productive farmland that was not protected from encroachment as municipalities expanded and opened pristine areas to development. The encroachment continues as rural citizens and their families are attracted to urban centers for the way of life they seemed to offer. This starts with employment possibilities, education for workers children, and family health services as manufacturing/industrial operations and large and small businesses that serviced them spurs economic development. Agricultural projects are located mainly away from population centers, be they major metropolises or smaller cities. They provide food security for urban citizens through “brought into markets” in-ground crops, bush crops, and tree crops they cultivate, and through animal husbandry (e. g. , beef and dairy cattle, poultry, hogs, sheep). The last 6. 2 Food Insecurity: Stemming UrbanizationMany in the global population especially babies, young children, and the aged in Africa and the suffered with chronic malnutrition and starvation from famine in 2017. This is the result of drought, people being displaced from their homes by warring factions and decisions not to allow humanitarian food supplies to reach starving populations, thus exacerbating the famine problem. [SEP] Justify:

Output: The text contrasts the claim by stating that agricultural projects are primarily located away from population centers, which implies that urban agriculture alone may not be sufficient to meet the food demands of major cities. It highlights that food security for urban citizens relies on crops and animal husbandry brought into markets, indicating that urban agriculture cannot fully fulfill the food needs without external resources.