[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3859":3,"related-3859":92},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":47,"slug":50,"view_count":51,"doi":52,"paper":53,"created_at":91},3859,"Large language models for social governance: An analytical framework and research agenda","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fnsr\u002Fnwag602","Abstract As social governance becomes increasingly complex, traditional governance approaches face challenges such as information asymmetry, fragmented coordination, delayed risk response, and uneven public service delivery. Large language models (LLMs) offer new possibilities for knowledge processing, decision support, and human–machine interaction in this context. This article provides a comprehensive and framework-oriented review of research on LLMs, digital governance, public governance, and computational social science. It develops a governance-oriented analytical framework that links LLMs capabilities, governance functions, operating mechanisms, governance outcomes, and risk constraints. The review examines representative application scenarios, including emergency management, information propagation, intelligent rule of law, data governance, public security, healthcare, and e-government. The analysis highlights uneven evidence maturity across these scenarios: bounded tasks such as policy retrieval, public consultation, information summarization, and administrative document processing have clearer deployment pathways, whereas high-stakes simulation, legal reasoning, public security assessment, healthcare triage, and cross-departmental decision support require stronger validation and oversight. LLM-enabled social governance also remains constrained by hallucination, algorithmic bias, privacy leakage, weak explainability, resource inequality, accountability gaps, and institutional adaptation challenges. This article argues that LLMs should be understood as decision-support infrastructures embedded in accountable, auditable, and public value-oriented governance institutions.","摘要 随着社会治理日益复杂，传统治理方式面临信息不对称、协调碎片化、风险响应滞后、公共服务供给不均等挑战。大语言模型（LLMs）为此情境下的知识处理、决策支持与人机交互提供了新的可能。本文对LLMs、数字治理、公共治理与计算社会科学的研究进行了综合性、框架导向的综述，构建了一个面向治理的分析框架，将LLMs能力、治理功能、运行机制、治理结果与风险约束相联结。综述考察了代表性应用场景，包括应急管理、信息传播、智慧法治、数据治理、公共安全、医疗健康与电子政务。分析表明，这些场景的证据成熟度不均衡：政策检索、公众咨询、信息摘要与行政文书处理等边界明确的任务具有更清晰的部署路径，而高风险模拟、法律推理、公共安全评估、医疗分诊与跨部门决策支持则需要更强的验证与监督。LLMs赋能的社会治理还受到幻觉、算法偏见、隐私泄露、可解释性薄弱、资源不平等、问责缺口与制度适应挑战等制约。本文认为，应将LLMs理解为嵌入可问责、可审计且面向公共价值的治理机构之中的决策支持基础设施。",null,"National Science Review","2026-09-27T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文探讨大语言模型在社会治理中的应用框架，属公共管理与计算社会科学范畴，与三农、农业信息化、智慧农业、数字乡村等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"大语言模型","数字治理","社会治理","公共管理",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":46},[32,40],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":39},"I2",4,true,[],[38],"decision support",[21],{"code":41,"score":42,"supported":14,"title":43,"abstract":44,"tags":45},"G5",1,[],[],[22],"农业相关性需要复核",[48,49],"大语言模型 公共管理 数字治理 社会治理","大语言模型 公共管理","大语言模型公共管理数字治理社会治理-3859",2,"10.1093\u002Fnsr\u002Fnwag602",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":15,"oa_url":83,"card":84,"direction":88,"ingested_from":90},"W7214589214",[56,58,61,62,64,66,67,70,73,75,76,77,80],{"name":57,"orcid":9},"Jun Hao",{"name":59,"orcid":60},"Jian Li","https:\u002F\u002Forcid.org\u002F0000-0002-4549-0694",{"name":59,"orcid":60},{"name":63,"orcid":9},"Linyuan Lü",{"name":65,"orcid":9},"Fang Wang",{"name":65,"orcid":9},{"name":68,"orcid":69},"Yijun Liu","https:\u002F\u002Forcid.org\u002F0009-0008-6601-9237",{"name":71,"orcid":72},"Jianjun Yu","https:\u002F\u002Forcid.org\u002F0000-0002-5203-171X",{"name":74,"orcid":9},"Changyong Liang",{"name":71,"orcid":72},{"name":74,"orcid":9},{"name":78,"orcid":79},"Xiaolong Zheng","https:\u002F\u002Forcid.org\u002F0000-0003-0405-5458",{"name":81,"orcid":82},"Zhixin Liu","https:\u002F\u002Forcid.org\u002F0000-0003-2342-4298","https:\u002F\u002Facademic.oup.com\u002Fnsr\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fnsr\u002Fnwag602\u002F71450964\u002Fnwag602.pdf",{"tldr":85,"method":86,"finding":87,"direction":88,"opportunity":89},"综述大语言模型在社会治理中的应用，构建分析框架并提出研究议程。","文献综述，整合LLM、数字治理与计算社会科学研究。","低风险任务部署路径清晰，高风险场景需验证与监管，LLM应作为决策支持基础设施。","数字乡村与农业信息化","可探索LLM在乡村治理、农业政策咨询等低风险场景的落地与评估框架。","openalex","2026-09-30T23:30:46.382519Z",{"total":93,"page":42,"page_size":93,"items":94},6,[95,179,233,317,382,414],{"id":96,"title":97,"url":98,"summary":99,"summary_zh":100,"content":9,"source_name":101,"source_url":98,"published_at":102,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":103,"score_detail":104,"sources":111,"tags":113,"primary_topic":118,"secondary_topics":124,"classification":129,"search_phrases":152,"slug":155,"view_count":15,"doi":156,"paper":157,"created_at":178},5376,"Agri-Synergy: A Hardware–Software Co-Designed Hybrid IoT System for Sensor Interface Standardization and LLM-Driven Interactive Decision Support","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriengineering8100446","This paper presents Agri-Synergy, a hybrid Internet of Things (IoT) system featuring a hardware–software co-design to tackle sensor data heterogeneity and the barriers to effective AI-driven decision support in smart agriculture. Unlike software-based data harmonization, we propose a universal sensor printed circuit board (PCB) with RS485\u002FModbus interface for hardware unification, complemented by on-node edge processing that ensures standardized data from source. To lower the entry barrier for farmers, the system integrates the DeepSeek large language model (LLM) API, combining static agricultural knowledge with real-time sensor data, for context-aware, natural language-based decision support, functioning without local model deployment. Field deployment showed reference-verified sensor accuracy predominantly within manufacturer specifications, stable 4G connectivity during the 4-h benchmark but intermittent dropouts during the 20-day deployment, and remote-control success rates of 96% (normal) and 80% (weak signal at 300 m). Multi-node concurrent transmission resulted in average latency of 238.0 ± 27.1 ms and packet loss below 0.5%. The system achieves a 3–5× cost reduction compared to commercial platforms. These results demonstrate that addressing data heterogeneity from source through hardware unification and software edge processing, while leveraging AI to lower the barrier to system adoption, provides a more reliable foundation for data synchronization and accessible AI-driven analytics.","本文提出Agri-Synergy，一种采用软硬件协同设计的混合物联网（IoT）系统，旨在解决智慧农业中传感器数据异构性问题以及有效AI驱动决策支持的障碍。与基于软件的数据协调方式不同，我们提出了一种带有RS485\u002FModbus接口的通用传感器印刷电路板（PCB）以实现硬件统一，并辅以节点端边缘处理，确保从源头获得标准化数据。为降低农民的使用门槛，该系统集成了DeepSeek大语言模型（LLM）API，将静态农业知识与实时传感器数据相结合，提供上下文感知的、基于自然语言的决策支持，且无需本地部署模型即可运行。现场部署表明，经参考验证的传感器精度基本处于制造商规格范围内；在4小时基准测试期间4G连接稳定，但在20天部署期间出现间歇性断连；远程控制成功率分别为96%（正常信号）和80%（300 m处弱信号）。多节点并发传输的平均延迟为238.0 ± 27.1 ms，丢包率低于0.5%。与商业平台相比，该系统实现了3–5倍的成本降低。这些结果表明，通过硬件统一和软件边缘处理从源头解决数据异构性问题，同时利用AI降低系统采用门槛，可为数据同步和可及的AI驱动分析提供更可靠的基础。","AgriEngineering","2026-10-19T00:00:00Z",76,{"impact":105,"substance":106,"depth":107,"authority":108,"freshness":109,"relevant":42,"comment":110},16,21,18,12,9,"硬件统一接口与边缘处理结合大模型决策支持的农业物联网系统论文，方法新颖、有实地部署数据，但属细分技术进展，公共影响有限。",[112],{"name":101,"url":98},[114,115,116,21,117],"智慧农业","农业人工智能","农业物联网","传感器标准化",{"code":119,"slug":120,"name":121,"path":122,"parent_code":123},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot","I",[125],{"code":33,"slug":126,"name":127,"path":128,"parent_code":123},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":130,"locked":14,"version":28,"method":29,"evidence":131},"classified",{"candidates":132,"agricultural_evidence":35},[133,140,145],{"code":119,"score":107,"supported":35,"title":134,"abstract":136,"tags":139},[135],"IoT",[137,138,135],"物联网","internet of things",[116],{"code":33,"score":105,"supported":35,"title":141,"abstract":142,"tags":144},[38],[143,38],"large language model",[115,21],{"code":146,"score":147,"supported":35,"title":148,"abstract":149,"tags":151},"I1",7,[],[114,150],"smart agriculture",[114],[153,154],"Agri-Synergy 物联网 传感器","DeepSeek 决策支持","Agri-Synergy物联网传感器-5376","10.3390\u002Fagriengineering8100446",{"doi":156,"openalex_id":158,"authors":159,"venue":101,"cited_by_count":15,"oa_url":98,"card":172,"direction":176,"ingested_from":90},"W7222920424",[160,162,164,166,168,170],{"name":161,"orcid":9},"Leqi Lai",{"name":163,"orcid":9},"Langtao Duan",{"name":165,"orcid":9},"Yuhao Chen",{"name":167,"orcid":9},"Yuhang Zheng",{"name":169,"orcid":9},"Jinyu Yang",{"name":171,"orcid":9},"Mingyao Wen",{"tldr":173,"method":174,"finding":175,"direction":176,"opportunity":177},"提出软硬件协同的混合物联网系统，统一传感器接口并用大模型提供交互式决策支持。","通用RS485\u002FModbus传感器PCB、节点边缘处理、DeepSeek大模型A","传感器精度达标，多节点延迟238ms、丢包低于0.5%，成本降低3-5倍。","智慧农业 \u002F 农业物联网","可研究弱信号下通信可靠性提升与边缘-云端LLM协同的长期田间鲁棒性。","2026-10-20T23:30:08.319698Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":9,"content":9,"source_name":184,"source_url":9,"published_at":185,"category":186,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":192,"tags":194,"primary_topic":197,"secondary_topics":202,"classification":203,"search_phrases":228,"slug":231,"view_count":15,"doi":9,"paper":9,"created_at":232},5334,"让 AI 赋能民族乡高质量发展:从经验农业到数据农业的关键一跃","https:\u002F\u002Fbig5.china.com.cn\u002Fgate\u002Fbig5\u002Fszjj.china.com.cn\u002F2026-10\u002F14\u002Fcontent_43497785.html","评论指出 AI 应成为民族乡田野上的新农具。贵州雷山用 AI 导游+云端茶园盘活苗岭资源,广西崇左以 AI 重构甘蔗糖业链条;民族乡多语言、多习俗、山寨分散,治理成本高,数字乡村高质量发展行动计划(2026—2030 年)明确提出远程医疗、智慧政务、数字健康向边远地区倾斜。同时强调要防止技术悬浮(设备闲置、群众不会用)和文化失语(算法把民族故事包装成流量模板)。AI 须懂方言、界面要适老、数据权属要清晰,非遗内容要人工复核。","中国网 2026-10-14","2026-10-14T00:00:00Z","报道",64,{"impact":107,"substance":105,"depth":189,"authority":108,"freshness":190,"relevant":42,"comment":191},15,3,"央媒评论结合贵州雷山、广西崇左案例与数字乡村行动计划，观点有实质，但属评论性报道且已发布近一周，时效偏弱，可作主题聚合而非每日精选。",[193],{"name":184,"url":182},[195,114,115,22,196],"数字乡村","民族地区",{"code":198,"slug":199,"name":200,"path":201,"parent_code":123},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village",[],{"status":130,"locked":14,"version":28,"method":29,"evidence":204},{"candidates":205,"agricultural_evidence":35},[206,210,216,220,224],{"code":198,"score":34,"supported":35,"title":207,"abstract":208,"tags":209},[],[195],[195],{"code":211,"score":190,"supported":35,"title":212,"abstract":213,"tags":215},"A6",[],[214],"甘蔗",[],{"code":41,"score":42,"supported":14,"title":217,"abstract":218,"tags":219},[],[],[22],{"code":146,"score":42,"supported":14,"title":221,"abstract":222,"tags":223},[],[],[114],{"code":33,"score":42,"supported":14,"title":225,"abstract":226,"tags":227},[],[],[115],[229,230],"雷山 云端茶园 AI导游","崇左 甘蔗糖业 AI","雷山云端茶园AI导游-5334","2026-10-20T02:47:36.879141Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":247,"primary_topic":250,"secondary_topics":251,"classification":252,"search_phrases":299,"slug":302,"view_count":15,"doi":303,"paper":304,"created_at":316},5232,"Current applications of artificial intelligence for advancing agriculture, sustainability and climate mitigation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02540-1","Artificial intelligence (AI) is reshaping agriculture by enabling data-driven decision-making across crop production systems and supporting the transition toward more sustainable, resilient, and climate-smart farming. This review critically synthesizes advances in AI applications spanning precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and large language model (LLM)-based decision support systems. Beyond summarizing technological developments, the review evaluates their practical deployment, emphasizing that successful agricultural AI depends not only on predictive accuracy but also on model generalizability, explainability, economic feasibility, interoperability, and integration with existing agronomic knowledge. Emerging technologies including Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning—are discussed as important directions for overcoming limitations related to data scarcity, environmental variability, and scalable field implementation. The review highlights that responsible AI adoption requires transparent governance frameworks addressing data ownership, privacy, algorithmic fairness, cybersecurity, and equitable access, particularly for smallholder farming systems. Although numerous AI models report outstanding performance, widespread agricultural adoption remains constrained by cross-region validation, fragmented datasets, infrastructure requirements, and insufficient real-world deployment. Future progress will depend on multidisciplinary collaboration integrating advances in artificial intelligence, sensing technologies, robotics, crop science, and agricultural engineering to develop robust, trustworthy, and farmer-centered decision-support systems. By critically evaluating technological innovations and implementation challenges, this review provides a framework for understanding the current state of AI in agriculture while identifying research priorities that will shape the next generation of sustainable and climate-resilient agricultural systems.","人工智能（AI）正在重塑农业，通过在作物生产系统中实现数据驱动的决策，并支持向更可持续、更具韧性和气候智能型农业转型。本综述批判性地综合了AI应用的进展，涵盖精准灌溉、养分与土壤管理、产量预测、病虫害监测、计算机视觉、自主农业机器人以及基于大语言模型（LLM）的决策支持系统。除总结技术发展外，本综述还评估了其实际部署情况，强调农业AI的成功不仅取决于预测准确性，还取决于模型泛化能力、可解释性、经济可行性、互操作性以及与现有农学知识的整合。文中讨论了包括视觉Transformer、基础模型、数字孪生、多模态AI、检索增强生成以及联邦学习和持续学习在内的新兴技术，将其视为克服数据稀缺、环境变异性和田间可扩展实施等局限的重要方向。本综述强调，负责任的AI采用需要透明的治理框架，以解决数据所有权、隐私、算法公平性、网络安全和公平获取等问题，尤其是对小农耕作系统而言。尽管众多AI模型报告了卓越的性能，但农业领域的广泛采用仍受限于跨区域验证、碎片化数据集、基础设施要求以及现实世界部署不足。未来的进展将取决于多学科协作，整合人工智能、传感技术、机器人技术、作物科学和农业工程领域的进展，以开发稳健、可信且以农民为中心的决策支持系统。通过批判性评估技术创新和实施挑战，本综述提供了一个理解AI在农业中现状的框架，同时确定了将塑造下一代可持续和气候韧性农业系统的研究优先事项。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",83,{"impact":243,"substance":243,"depth":107,"authority":108,"freshness":109,"relevant":42,"comment":244},22,"该综述系统梳理了AI在农业中的应用与落地瓶颈，覆盖精准灌溉、机器人、大模型决策支持等方向，信息密度高，但属综述类论文，产业级突破性有限，可入精选。",[246],{"name":239,"url":236},[114,115,21,248,249],"数字孪生","气候智慧农业",{"code":33,"slug":126,"name":127,"path":128,"parent_code":123},[],{"status":130,"locked":14,"version":28,"method":29,"evidence":253},{"candidates":254,"agricultural_evidence":35},[255,263,270,277,282,288,294],{"code":33,"score":256,"supported":35,"title":257,"abstract":259,"tags":262},19,[258],"artificial intelligence",[260,258,261,143,38],"计算机视觉","computer vision",[115,21],{"code":264,"score":93,"supported":35,"title":265,"abstract":266,"tags":269},"H2",[],[267,268],"土壤","soil",[],{"code":271,"score":93,"supported":35,"title":272,"abstract":273,"tags":276},"H3",[],[274,275],"灌溉","irrigation",[],{"code":146,"score":278,"supported":35,"title":279,"abstract":280,"tags":281},5,[],[248],[248,114],{"code":283,"score":190,"supported":35,"title":284,"abstract":285,"tags":287},"F6",[],[286],"农业机器人",[],{"code":289,"score":190,"supported":35,"title":290,"abstract":291,"tags":293},"G2",[],[292],"smallholder",[],{"code":295,"score":42,"supported":14,"title":296,"abstract":297,"tags":298},"H7",[],[],[249],[300,301],"气候智慧农业 AI","农业人工智能 气候智慧农业 大语言模型 数字孪生","气候智慧农业AI-5232","10.1007\u002Fs44163-026-02540-1",{"doi":303,"openalex_id":305,"authors":306,"venue":239,"cited_by_count":15,"oa_url":236,"card":310,"direction":176,"ingested_from":90},"W7222823256",[307],{"name":308,"orcid":309},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":311,"method":312,"finding":313,"direction":314,"opportunity":315},"综述农业人工智能在精准灌溉、产量预测、病虫害监测等应用及落地挑战。","文献综述，覆盖视觉Transformer、基础模型、数字孪生、多模态AI与联邦学","农业AI落地受限于跨区域验证、数据碎片化与基础设施，需兼顾可解释性与公平治理。","农业人工智能与决策模型","可研究小农户场景下跨区域可泛化、可解释且低成本的农业大模型与联邦学习部署。","2026-10-18T23:30:07.666127Z",{"id":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":325,"sources":327,"tags":329,"primary_topic":333,"secondary_topics":337,"classification":338,"search_phrases":356,"slug":359,"view_count":51,"doi":360,"paper":361,"created_at":381},3909,"Principles of creating a National Spatial Data System","https:\u002F\u002Fdoi.org\u002F10.18799\u002F24131830\u002F2026\u002F9\u002F5868","The article analyzes in detail the processes of formation and development of the National Spatial Data System – an open system and the most important tool for the digitalization and systematization of open spatial data in Russia. Spatial information is of great importance for optimization of public administration processes (in the sphere of land and property relations), planning of territorial use, implementation of infrastructure projects, cadastral accounting, as well as for meeting the needs of the population, business and government agencies in the field of ecology, defense, agriculture, forestry and other industries. The authors focus on the importance of creating an integrated digital resource where real estate information cartographic data, and territorial planning will be collected on a single multifunctional platform. The paper considers in detail the goals and objectives of the state program for the creation of the National Spatial Data System. The development of domestic geoinformation software, increasing the completeness and quality of data in the Unified State Register of Real Estate and other information systems, achieving a high level of digital maturity, and improving the quality of public services are among them. The study presents the results of the system operation in four pilot regions: Perm Krai, the Republic of Tatarstan, Krasnodar Krai, and Irkutsk Oblast. The paper describes the obtained results , including the elimination of errors in the boundary register, data verification and provision of services for citizens, businesses, and government agencies. The National Spatial Data System is considered as the basis for building a unified geoinformation ecosystem, including analytics, forecasting, automated services and decision support. The paper introduces the key areas of interdepartmental interaction and technical integration. The authors make a conclusion on the project high significance for the implementation of the country spatial development strategy, including territorial planning, and for increasing the investment attractiveness of constituent entity of the Russian Federation. For citation: Martynova E.V., Balandin V.A., Lipilin D.A., Gura D.A. Principles of creating a National Spatial Data System. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 9, pp. 190–199. http:\u002F\u002Fdoi.org\u002F10.18799\u002F24131830\u002F2026\u002F9\u002F5868","本文详细分析了国家空间数据系统（National Spatial Data System）的形成与发展过程——这是一个开放系统，也是俄罗斯开放空间数据数字化和系统化的重要工具。空间信息对于优化公共管理流程（土地与财产关系领域）、领土利用规划、基础设施项目实施、地籍核算，以及满足居民、企业和政府机构在生态、国防、农业、林业等行业的需求具有重要意义。作者重点关注创建一体化数字资源的重要性，即将不动产信息、制图数据和领土规划汇集于一个多功能平台。本文详细探讨了创建国家空间数据系统的国家计划的目标与任务，其中包括：发展国产地理信息软件，提高统一国家不动产登记簿及其他信息系统中数据的完整性和质量，达到高水平的数字化成熟度，以及提升公共服务质量。研究展示了该系统在四个试点地区（彼尔姆边疆区、鞑靼斯坦共和国、克拉斯诺达尔边疆区和伊尔库茨克州）的运行结果。本文描述了所取得的成果，包括消除边界登记中的错误、数据核查以及为公民、企业和政府机构提供服务。国家空间数据系统被视为构建统一地理信息生态系统的基础，涵盖分析、预测、自动化服务和决策支持。本文介绍了部门间协作和技术集成的关键方向。作者得出结论：该项目对于实施国家空间发展战略（包括领土规划）以及提高俄罗斯联邦主体投资吸引力具有重大意义。引用格式：Martynova E.V., Balandin V.A., Lipilin D.A., Gura D.A. Principles of creating a National Spatial Data System. Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, 2026, vol. 337, no. 9, pp. 190–199. http:\u002F\u002Fdoi.org\u002F10.18799\u002F24131830\u002F2026\u002F9\u002F5868","Bulletin of the Tomsk Polytechnic University Geo Assets Engineering","2026-09-30T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":326},"俄罗斯国家空间数据系统论文，属测绘地理信息与数字治理范畴，与三农、农业信息化无直接关联，不予入选。",[328],{"name":323,"url":320},[22,330,331,332],"地理信息","空间数据系统","不动产登记",{"code":123,"slug":334,"name":335,"path":336,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":339,"locked":14,"version":28,"method":29,"evidence":340},"partial",{"candidates":341,"agricultural_evidence":35,"reason":355},[342,346,351],{"code":33,"score":190,"supported":35,"title":343,"abstract":344,"tags":345},[],[38],[],{"code":347,"score":190,"supported":35,"title":348,"abstract":349,"tags":350},"I4",[],[330],[],{"code":41,"score":42,"supported":14,"title":352,"abstract":353,"tags":354},[],[],[22],"一级领域明确，二级方向需要细分",[357,358],"俄罗斯 国家空间数据系统","统一不动产登记 地理信息","俄罗斯国家空间数据系统-3909","10.18799\u002F24131830\u002F2026\u002F9\u002F5868",{"doi":360,"openalex_id":362,"authors":363,"venue":323,"cited_by_count":15,"oa_url":320,"card":376,"direction":88,"ingested_from":90},"W7214930429",[364,367,370,373],{"name":365,"orcid":366},"Elena V. Martynova","https:\u002F\u002Forcid.org\u002F0009-0007-3068-887X",{"name":368,"orcid":369},"Vasily A. Balandin","https:\u002F\u002Forcid.org\u002F0009-0009-3175-3252",{"name":371,"orcid":372},"Dmitry A. Lipilin","https:\u002F\u002Forcid.org\u002F0000-0002-3766-6063",{"name":374,"orcid":375},"Dmitry A. Gura","https:\u002F\u002Forcid.org\u002F0000-0002-2748-9622",{"tldr":377,"method":378,"finding":379,"direction":88,"opportunity":380},"分析俄罗斯国家空间数据系统的构建原则、目标及四个试点地区运行成效。","分析国家空间数据系统项目文件与四个试点地区运行结果。","该系统是统一地理信息生态基础，可支撑决策、规划并提升投资吸引力。","可研究国家空间数据平台如何与农业用地管理、确权及乡村规划数据对接。","2026-10-01T23:30:39.457998Z",{"id":383,"title":384,"url":385,"summary":386,"summary_zh":9,"content":387,"source_name":388,"source_url":9,"published_at":389,"category":186,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":390,"sources":392,"tags":394,"primary_topic":9,"secondary_topics":397,"classification":398,"search_phrases":409,"slug":412,"view_count":51,"doi":9,"paper":9,"created_at":413},3694,"智治平台托起群众身边小事","https:\u002F\u002Fwww.zyshgzb.gov.cn\u002Fn1\u002F2026\u002F0924\u002Fc462652-40805045.html","浙江缙云迭代升级141基层治理体系，融合矛盾纠纷调处、社会治安防控、应急管理指挥、社会工作服务四大板块。游客反映景区商户强买强卖问题当日完成核查处置反馈。贵州新蒲新区六角井社区智慧管理主控平台联动居民使用端口，AI智慧眼系统一分钟内完成消防通道工单派发。","数字技术是推进基层治理现代化的重要支撑。当前，各地党委社会工作部门立足基层治理实际，把数字化建设作为破题抓手，搭建智治平台、打通数据壁垒、重塑处置流程，推动基层治理从被动处置向主动服务、分散管理向系统集成、线下跑腿向云端智办转变，不断释放治理效能，让群众切实感受到可感可及的民生温度。\n\n平台集成是数字赋能基层治理的基础，通过整合分散资源，破解条块分割、多头管理等难题。浙江省丽水市缙云县以迭代升级“141”基层治理体系为总牵引，重构基层治理平台，迭代矛盾纠纷调处、社会治安防控、应急管理指挥、社会工作服务等四大板块功能，融合乡镇岗位管理改革，健全闭环运行体系，理顺“以块为主、条块结合”运行模式，实现跨部门快速联动处置。\n\n“反映的问题在一天内就完成联合处置，效率太高了。”一位来到缙云县的游客由衷赞叹。针对游客反映的某景区商户强买强卖问题，缙云县委社会工作部管理社会治理中心依托“141”基层治理体系联动优势，快速统筹市场监管、旅游主管部门及属地乡镇，组建联合执法队伍，当日便完成核查、处置、反馈全流程，切实维护游客合法权益，彰显了基层治理的速度与力度。\n\n此外，缙云县打造“云上社区”平台，归集民政、人社、医保等20余个部门150余项便民服务，吸纳百余家社会组织入驻，群众可以一键上报隐患、查询政策、反馈诉求，构建“指尖办事、线上共治”的基层治理新模式，不断提升群众的获得感、幸福感、安全感。\n\n贵州省遵义市新蒲新区六角井社区创新引入智慧社区建设试点项目，通过搭建智慧管理主控平台（电脑端）、工作人员使用端口（APP）、居民使用端口（小程序）三个主要端口，联动激活社区、网格员和居民群众手中的“三块屏幕”，以高效率数智赋能推动打造高质量智慧社区。新蒲新区礼仪街道新民社区依托人工智能“智慧眼”系统，能够实现人脸识别、火情预警等智能分析，有效降低社区安全风险。社区一栋单元楼的消防通道被外来车辆占用后，人工智能系统迅速捕捉异常并触发预警，一分钟内完成工单派发，社区工作者快速抵达现场劝导，消防通道迅速恢复畅通。\n\n数字平台不仅是技术载体，更是治理资源的整合枢纽，通过平台重构，打破了部门壁垒，实现治理力量的集约调度。\n\n畅通需求渠道、健全闭环处置机制，让群众需求事事有回音，是数字治理的目标。山西省吕梁市中阳县创新探索“书记接单马上就办”云端便民服务，在直播间搭建政民互动直通车，通过弹幕留言、在线连麦、后台私信3种方式，建立线上反映渠道，同时健全“群众点单—平台接单—分类派单—限时办理—群众回访—办结销号”全链条闭环机制，明确办理责任与时限，组建督查小组动态督办，办结事项公开公示，接受群众评价监督。同步开展线下入户走访，兼顾不同群体需求，推动便民服务从群众上门求助向干部主动响应转变。\n\n从“事后处置”转向“未诉先办”，数字技术正推动治理逻辑发生根本性转变。四川省成都市武侯区坚持党建引领，立足实用管用、惠民便民导向，打造一系列本土化智慧治理场景，推动服务端口前移，实现民情早知晓、难题早化解、服务快落地，让智慧治理惠及千家万户。该区打造“社区主题数据库”，整合相关数据，依托智能算法自动沉淀、动态更新、实时校准，兼具自定义查询、数据导出、工作预判等功能，让社区治理实现底数清、靶向准、服务实。武侯区晋阳街道吉福社区立足老年居民占比高的实际情况，建设智慧应用场景，安装智能防摔倒感应装置，一旦监测到异常情况，第一时间向物业服务企业、微网格长推送预警信息，实现风险早发现、隐患快处置，全力守护老年人居家安全。\n\n数字赋能基层治理，技术是手段，为民是根本。数字化改革绝非简单把线下工作搬到线上，而是机制重塑、资源整合、服务升级的系统性工程。各地党委社会工作部门坚持党建引领，以群众获得感作为检验成效标尺，持续优化数字平台功能，完善网格与数字平台联动机制，引导社会组织、群众广泛参与，让数字技术真正扎根基层土壤，不断绘就共建共治共享的基层治理画卷。","中共中央社会工作部","2026-09-24T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":391},"内容为基层数字治理与智慧社区实践，属社会治理范畴，与三农、农业信息化、智慧农业、数字乡村等主题无直接关联，不建议进入每日精选。",[393],{"name":388,"url":385},[195,22,395,396],"基层治理","智慧社区",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":399},{"candidates":400,"agricultural_evidence":14,"reason":46},[401,405],{"code":41,"score":278,"supported":35,"title":402,"abstract":403,"tags":404},[],[395],[395,22],{"code":198,"score":42,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[195],[410,411],"缙云县 基层治理体系","新蒲新区 智慧社区 试点","缙云县基层治理体系-3694","2026-09-29T00:08:18.894955Z",{"id":415,"title":416,"url":417,"summary":418,"summary_zh":419,"content":9,"source_name":420,"source_url":417,"published_at":421,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":422,"score_detail":423,"sources":428,"tags":430,"primary_topic":433,"secondary_topics":439,"classification":440,"search_phrases":470,"slug":473,"view_count":42,"doi":474,"paper":475,"created_at":486},3665,"ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The Evolution of Large Language Models and Their Impact on Animal Sciences","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fjas\u002Fskag306","The rapid rise of large language models (LLM) is reshaping the scientific landscape, transitioning from early statistical language models to advanced transformer-based architectures capable of synthesizing knowledge across disciplines. While their predictive capacity and scalability have opened new avenues in data analysis, hypothesis generation, and decision support, concerns remain regarding bias, hallucination, reproducibility, and ethical governance. In animal sciences, LLM are gradually applied to challenges in nutrition modeling, animal health, genetic selection, and sustainability. Precision nutrition has benefited from LLM-driven synthesis of feed and metabolic data, enabling individualized feeding strategies and improved resource efficiency. In animal health, domain-specific systems have demonstrated applications in diagnostics and epidemiological monitoring. LLM is augmenting genomic analyses to accelerate marker discovery and breeding optimization, while sustainability efforts employ them to model greenhouse gas emissions, feed additives, and adaptation to climatic stressors. Notably, decision-support platforms demonstrate how domain-specialized LLM can bridge mechanistic knowledge with predictive analytics, enhancing knowledge transfer and empowering livestock producers. However, risks associated with overreliance, recursive reuse of LLM outputs in model development, and pseudo-expertise underscore the critical importance of human oversight. Unlike mechanistic models, which embed biological causality, LLM are entirely data-driven and may confidently propagate errors if trained on ill-conditioned datasets. Responsible use requires transparent reporting, validation, and bias auditing, with domain-specific fine-tuning. Open-source models can enhance reproducibility and trust, but they also raise financial and security concerns. LLM must be guided by transparency, accountability, and fairness to ensure they complement, rather than replace, human expertise. By advancing inquiry and livestock management, LLM hold the potential to support sustainable food production systems if deployed responsibly. Rather than full training, most applications will rely on fine-tuning and augmentation, which are more sustainable and adaptable strategies. This review intends to promote critical literacy and responsible adoption of LLM in research, education, and livestock decision-support systems for animal scientists, nutrition modelers, and extension professionals.","大语言模型（LLM）的迅速崛起正在重塑科学格局，从早期的统计语言模型过渡到能够跨学科综合知识的先进基于Transformer的架构。尽管其预测能力和可扩展性在数据分析、假设生成和决策支持方面开辟了新途径，但围绕偏见、幻觉、可重复性和伦理治理的担忧依然存在。在动物科学领域，LLM正逐步应用于营养建模、动物健康、遗传选择和可持续性等方面的挑战。精准营养已受益于LLM驱动的饲料与代谢数据综合，从而实现个体化饲喂策略并提高资源效率。在动物健康方面，领域专用系统已在诊断和流行病学监测中展现出应用价值。LLM正在增强基因组分析，以加速标记发现和育种优化，而可持续性工作则利用其建模温室气体排放、饲料添加剂及对气候胁迫的适应性。值得注意的是，决策支持平台展示了领域专用LLM如何将机制性知识与预测分析相衔接，促进知识转化并赋能畜牧生产者。然而，过度依赖、在模型开发中递归复用LLM输出以及伪专业知识等风险，凸显了人类监督的至关重要性。与嵌入生物学因果关系的机制性模型不同，LLM完全由数据驱动，若在病态数据集上训练，可能自信地传播错误。负责任的使用需要透明报告、验证和偏见审计，并进行领域专用微调。开源模型可以增强可重复性和信任，但也引发财务和安全方面的担忧。LLM必须以透明性、问责制和公平性为指引，以确保其补充而非取代人类专业知识。通过在研究和畜牧管理中推动探究，LLM在负责任部署的前提下具有支持可持续食品生产系统的潜力。大多数应用将依赖微调和增强，而非完整训练，这是更具可持续性和适应性的策略。本综述旨在促进动物科学家、营养建模人员和推广专业人员在研究、教育和畜牧决策支持系统中对LLM的批判性素养和负责任采用。","Journal of Animal Science","2026-09-25T00:00:00Z",78,{"impact":107,"substance":424,"depth":107,"authority":425,"freshness":426,"relevant":42,"comment":427},20,14,8,"核心期刊综述，系统梳理大语言模型在动物营养、健康与育种中的应用与风险，专业深度高但属综述性论文，产业级影响有限。",[429],{"name":420,"url":417},[114,115,21,431,432],"精准营养","动物科学",{"code":434,"slug":435,"name":436,"path":437,"parent_code":438},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[],{"status":130,"locked":14,"version":28,"method":29,"evidence":441},{"candidates":442,"agricultural_evidence":35},[443,449,456,460,466],{"code":434,"score":93,"supported":35,"title":444,"abstract":445,"tags":448},[],[446,447],"温室","greenhouse",[],{"code":450,"score":93,"supported":35,"title":451,"abstract":452,"tags":455},"C",[],[453,454],"畜牧","livestock",[],{"code":33,"score":278,"supported":35,"title":457,"abstract":458,"tags":459},[],[38],[115,21],{"code":461,"score":190,"supported":35,"title":462,"abstract":463,"tags":465},"C4",[],[464],"饲料",[],{"code":146,"score":42,"supported":14,"title":467,"abstract":468,"tags":469},[],[],[114],[471,472],"大语言模型 动物科学","LLM 精准营养","大语言模型动物科学-3665","10.1093\u002Fjas\u002Fskag306",{"doi":474,"openalex_id":476,"authors":477,"venue":420,"cited_by_count":15,"oa_url":480,"card":481,"direction":176,"ingested_from":90},"W7214347972",[478],{"name":479,"orcid":9},"Luis O Tedeschi","https:\u002F\u002Facademic.oup.com\u002Fjas\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fjas\u002Fskag306\u002F71398322\u002Fskag306.pdf",{"tldr":482,"method":483,"finding":484,"direction":314,"opportunity":485},"综述大语言模型在动物科学中的应用、风险与负责任使用路径。","文献综述，梳理LLM在营养、健康、遗传和可持续性中的应用。","LLM可辅助精准营养与决策支持，但需人类监督、验证和领域微调。","可探索面向畜牧生产的领域专用LLM微调与可验证决策支持系统，并建立偏差审计与可复现评估框架。","2026-09-28T23:30:12.831387Z"]