[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2340":3,"related-2340":120},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":79,"created_at":119},2340,"A real-time forecasting framework for emerging infectious diseases affecting animal populations","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pcbi.1014716","Infectious disease forecasting has become increasingly important in public health. However, forecasting tools for emergency animal diseases, particularly those offering real-time decision support when parameters governing disease dynamics are unknown, remain limited. We introduce a generalised modelling framework for near-real-time forecasting of the temporal and spatial spread of infectious livestock diseases using data from the early stages of an outbreak. We applied the framework to the 2007 equine influenza outbreak in Australia, generating forecasts at three timepoints across four regional clusters. Prediction targets included future daily case counts, outbreak size, peak timing and duration, and spatial distributions of future spread. We evaluated how well the forecasts predicted daily cases and the spatial distribution of case counts, using skill scores (a measure of probabilistic forecast accuracy) as a benchmark for future model improvements. Forecast accuracy, certainty, and skill improved after formation of the outbreak's peak, while early forecasts were more uncertain or prone to overestimation, highlighting the need for caution when interpreting pre-peak predictions, particularly when the impacts of control policies on future transmission are not adequately represented in the model. Spatial forecasts of broad, relative risk patterns were more robust than precise predictions of risk at the individual premises level or exact daily cases counts, supporting geographically targeted response strategies. Overall, this framework supports real-time decision-making in livestock disease outbreaks when applied with appropriate consideration of uncertainty, and establishes a foundation for future refinements and applications to other animal diseases.","传染病预测在公共卫生领域日益重要。然而，针对紧急动物疫病的预测工具，尤其是能够在疾病动态参数未知情况下提供实时决策支持的工具，仍然十分有限。我们提出了一种广义建模框架，可利用疫情早期阶段的数据对牲畜传染病的时空传播进行近实时预测。我们将该框架应用于2007年澳大利亚马流感疫情，在四个区域集群的三个时间节点上生成了预测。预测目标包括未来每日病例数、疫情规模、峰值时间和持续时间，以及未来传播的空间分布。我们评估了预测对每日病例数和病例数空间分布的预测效果，并使用技巧评分（skill score，一种概率预测准确度的度量）作为未来模型改进的基准。在疫情峰值形成后，预测的准确性、确定性和技巧均有所提高，而早期预测则更为不确定或倾向于高估，这凸显了在解读峰值前预测时需谨慎，尤其是当控制政策对未来传播的影响未在模型中得到充分体现时。对宏观相对风险模式的空间预测比对个体场所层面风险的精确预测或精确每日病例数的预测更为稳健，这为地理靶向应对策略提供了支持。总体而言，该框架在适当考虑不确定性的前提下，可支持牲畜疫病暴发期间的实时决策，并为未来的改进及应用于其他动物疫病奠定了基础。",null,"PLoS Computational Biology","2026-09-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,14,7,1,"核心期刊发表的动物疫病实时时空预测建模研究，方法新颖、结论可靠，对畜牧疫病精准防控与应急决策有实质参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"决策支持","空间分析","动物疫病","疫情预测","智慧畜牧",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,58,63,68],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},4,true,[],[49],"decision support",[26],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":57},"C",3,[],[56],"livestock",[],{"code":59,"score":53,"supported":46,"title":60,"abstract":61,"tags":62},"C5",[],[28],[],{"code":64,"score":21,"supported":14,"title":65,"abstract":66,"tags":67},"C6",[],[],[30],{"code":69,"score":21,"supported":14,"title":70,"abstract":71,"tags":72},"I4",[],[],[27],[74,75],"决策支持 动物疫病 智慧畜牧 疫情预测","决策支持 动物疫病","决策支持动物疫病智慧畜牧疫情预测-2340",2,"10.1371\u002Fjournal.pcbi.1014716",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":110,"oa_url":111,"card":112,"direction":116,"ingested_from":118},"W7212183958",[82,85,88,91,93,96,98,100,103,105,107],{"name":83,"orcid":84},"Meryl Theng","https:\u002F\u002Forcid.org\u002F0000-0002-1016-1487",{"name":86,"orcid":87},"Simin Lee","https:\u002F\u002Forcid.org\u002F0000-0003-2631-9899",{"name":89,"orcid":90},"Andrew C. Breed","https:\u002F\u002Forcid.org\u002F0000-0002-3439-9510",{"name":92,"orcid":9},"Sharon Roche",{"name":94,"orcid":95},"Emily Sellens","https:\u002F\u002Forcid.org\u002F0000-0001-9360-1419",{"name":97,"orcid":9},"Catherine Fraser",{"name":99,"orcid":9},"Kelly Wood",{"name":101,"orcid":102},"Chris Jewell","https:\u002F\u002Forcid.org\u002F0000-0002-7902-2178",{"name":104,"orcid":9},"Mark A. Stevenson",{"name":106,"orcid":9},"Chris Baker",{"name":108,"orcid":109},"Simon M. Firestone","https:\u002F\u002Forcid.org\u002F0000-0002-3239-1419",0,"https:\u002F\u002Fjournals.plos.org\u002Fploscompbiol\u002Farticle\u002Ffile?id=10.1371\u002Fjournal.pcbi.1014716&type=printable",{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"提出一个利用疫情早期数据实时预测牲畜传染病时空传播的通用建模框架。","基于2007年澳大利亚马流感疫情早期数据，在四个区域三个时间点生成概率预测。","峰值后预测精度与确定性提升，早期预测易高估；空间相对风险预测比精确病例数更稳健。","农业人工智能与决策模型","可将该实时预测框架迁移至其他动物疫病，并融合物联网监测数据提升早期预警能力。","openalex","2026-09-13T23:30:46.953723Z",{"total":121,"page":21,"page_size":121,"items":122},6,[123,203,267,342,400,453],{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":9,"source_name":129,"source_url":126,"published_at":130,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":131,"score_detail":132,"sources":137,"tags":139,"primary_topic":144,"secondary_topics":145,"classification":146,"search_phrases":176,"slug":179,"view_count":77,"doi":180,"paper":181,"created_at":202},3881,"A Human-Centered Machine Learning Dashboard for Real-Time Smart Farming Decision Support","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fshem.v2i2.302","The integration of machine learning (ML) into precision agriculture has advanced predictive accuracy for tasks such as crop monitoring, irrigation scheduling, and yield estimation. However, many existing IoT-based smart farming systems remain technically driven, offering limited attention to how farmers and field operators actually interact with and interpret model outputs. This gap often results in low adoption rates, as complex predictions fail to translate into actionable, trustworthy decisions for end users. This study proposes a human-centered machine learning dashboard designed to bridge predictive modeling with real-time, farmer-friendly decision support. The system integrates IoT sensor data streams with ML-based prediction modules, presented through an interactive, usability-tested interface that emphasizes clarity, accessibility, and interpretability over raw technical complexity. A working prototype was developed and evaluated using both quantitative usability metrics (e.g., task completion time, System Usability Scale scores) and qualitative feedback from target users to assess the dashboard's effectiveness in supporting field-level decisions. The proposed system architecture, dashboard interface, and underlying ML pipeline are implemented as open and reproducible source code, enabling replication and adaptation across similar smart farming contexts. Results indicate that the human-centered design significantly improves users' comprehension of model outputs and their confidence in acting on system recommendations, without compromising predictive performance. This research contributes a practical framework for designing machine intelligence systems that prioritize end-user experience alongside technical accuracy, offering implications for the broader development of human-centered AI in agricultural and IoT-driven decision support environments.","机器学习（ML）在精准农业中的融合提升了作物监测、灌溉调度和产量估算等任务的预测准确性。然而，许多现有的基于物联网的智慧农业系统仍然以技术驱动为主，对农民和田间操作人员实际如何与模型输出交互并加以解读关注有限。这一差距往往导致采用率偏低，因为复杂的预测无法转化为最终用户可操作、可信赖的决策。本研究提出了一种以人为中心的机器学习仪表盘，旨在将预测建模与实时、面向农民的决策支持相衔接。该系统将物联网传感器数据流与基于机器学习的预测模块相集成，并通过一个经过可用性测试的交互式界面呈现，强调清晰性、可及性和可解释性，而非原始的技术复杂性。我们开发了一个可运行的原型，并使用定量可用性指标（如任务完成时间、系统可用性量表评分）和来自目标用户的定性反馈进行评估，以考察该仪表盘在支持田间决策方面的有效性。所提出的系统架构、仪表盘界面和底层机器学习流水线均以开放且可复现的源代码形式实现，便于在类似的智慧农业场景中复制和适配。结果表明，以人为中心的设计显著提升了用户对模型输出的理解以及依据系统建议采取行动的信心，同时未损害预测性能。本研究为设计在技术准确性的同时优先考虑最终用户体验的机器智能系统提供了一个实用框架，对农业及物联网驱动的决策支持环境中以人为中心的人工智能的更广泛发展具有启示意义。","Smart Human-centered Emerging Research in Machine Intelligence","2026-09-30T00:00:00Z",64,{"impact":133,"substance":17,"depth":134,"authority":13,"freshness":135,"relevant":21,"comment":136},12,16,8,"提出以人为本的机器学习仪表盘，兼顾可用性与预测性能，并开源实现，对智慧农业落地有参考价值。",[138],{"name":129,"url":126},[140,141,142,26,143],"智慧农业","农业人工智能","物联网","人机交互",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":147},{"candidates":148,"agricultural_evidence":46},[149,156,163,169],{"code":32,"score":150,"supported":46,"title":151,"abstract":153,"tags":155},27,[152,49],"machine learning",[154,152,49],"机器学习",[141,26],{"code":157,"score":13,"supported":46,"title":158,"abstract":159,"tags":162},"I1",[],[140,160,161],"精准农业","precision agriculture",[140],{"code":164,"score":20,"supported":46,"title":165,"abstract":166,"tags":168},"I5",[],[142,167],"IoT",[142],{"code":170,"score":121,"supported":46,"title":171,"abstract":172,"tags":175},"H3",[],[173,174],"灌溉","irrigation",[],[177,178],"农业人工智能 人机交互 决策支持 智慧农业","农业人工智能 人机交互","农业人工智能人机交互决策支持智慧农业-3881","10.68012\u002Fshem.v2i2.302",{"doi":180,"openalex_id":182,"authors":183,"venue":129,"cited_by_count":110,"oa_url":126,"card":196,"direction":200,"ingested_from":118},"W7214922384",[184,187,189,191,193],{"name":185,"orcid":186},"Adam Faturahman","https:\u002F\u002Forcid.org\u002F0000-0001-9727-9092",{"name":188,"orcid":9},"James Williams",{"name":190,"orcid":9},"Evelyn Anderson",{"name":192,"orcid":9},"Goh Yao",{"name":194,"orcid":195},"Muhammad Rifat Syauqy","https:\u002F\u002Forcid.org\u002F0009-0001-4582-5693",{"tldr":197,"method":198,"finding":199,"direction":200,"opportunity":201},"提出以人为中心的机器学习仪表盘，融合物联网与预测模型，为农户提供实时可解释的决策支持。","集成物联网传感器数据流与机器学习预测模块，开发交互式仪表盘，并用SUS量表和用户","以人为中心的设计显著提升用户对模型输出的理解与采纳信心，且不牺牲预测性能。","智慧农业 \u002F 农业物联网","可探索不同作物、地区和文化背景下农户对可解释AI界面的差异化需求与长期采纳效果。","2026-10-01T23:30:09.481397Z",{"id":204,"title":205,"url":206,"summary":207,"summary_zh":9,"content":9,"source_name":208,"source_url":206,"published_at":209,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":210,"score_detail":211,"sources":215,"tags":217,"primary_topic":220,"secondary_topics":226,"classification":228,"search_phrases":250,"slug":253,"view_count":53,"doi":254,"paper":255,"created_at":266},3807,"Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10698325\u002Fv1","Reliability Auditing and Capacity-Constrained Review for Wheat-Yield Decision Support under Temporal and Spatial Shift。Research Square","Research Square","2026-09-28T00:00:00Z",52,{"impact":135,"substance":19,"depth":212,"authority":121,"freshness":213,"relevant":21,"comment":214},15,9,"预印本论文，聚焦小麦产量决策支持系统的可靠性审计与时空迁移问题，方法有一定新意但尚未经同行评审，公共影响有限。",[216],{"name":208,"url":206},[140,141,26,218,219],"遥感监测","小麦产量",{"code":221,"slug":222,"name":223,"path":224,"parent_code":225},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[227],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":229},{"candidates":230,"agricultural_evidence":46},[231,236,241,245],{"code":32,"score":232,"supported":46,"title":233,"abstract":234,"tags":235},13,[49],[49],[141,26],{"code":221,"score":237,"supported":46,"title":238,"abstract":239,"tags":240},11,[222],[222],[],{"code":157,"score":21,"supported":14,"title":242,"abstract":243,"tags":244},[],[],[140],{"code":246,"score":21,"supported":14,"title":247,"abstract":248,"tags":249},"I3",[],[],[218],[251,252],"小麦产量 决策支持 遥感","Research Square 小麦 产量预测","小麦产量决策支持遥感-3807","10.21203\u002Frs.3.rs-10698325\u002Fv1",{"doi":254,"openalex_id":256,"authors":257,"venue":208,"cited_by_count":110,"oa_url":206,"card":9,"direction":116,"ingested_from":118},"W7214624863",[258,261,264],{"name":259,"orcid":260},"Xin Li","https:\u002F\u002Forcid.org\u002F0009-0005-0670-5006",{"name":262,"orcid":263},"Bojian Guo","https:\u002F\u002Forcid.org\u002F0009-0005-8353-2436",{"name":265,"orcid":9},"Asel Kartanova","2026-09-29T23:31:02.997580Z",{"id":268,"title":269,"url":270,"summary":271,"summary_zh":272,"content":9,"source_name":273,"source_url":270,"published_at":209,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":277,"tags":279,"primary_topic":283,"secondary_topics":284,"classification":285,"search_phrases":301,"slug":304,"view_count":77,"doi":305,"paper":306,"created_at":341},3806,"Data Management in Agricultural Enterprises: A Framework for Data-Driven Decision Support","https:\u002F\u002Fdoi.org\u002F10.7160\u002Faol.2026.180305","The growing digitalisation of agriculture is leading to a rapid increase in the volume of data produced by agricultural enterprises; however, its practical use is often limited by fragmented data sources, varying data quality, insufficient metadata, and inconsistent storage and management practices. The aim of this article is to propose a practical framework for the systematic management of data in agricultural enterprises to support its long-term availability, reuse, and integration into decision-making processes. The proposed approach is based on four interlinked steps: identifying data needs and issues, creating and recording a data inventory, drawing up a Data Management Plan (DMP), and implementing it in practice. The framework links technical aspects of data management – such as data formats, metadata, quality, integrity, security, backup and archiving – with the definition of responsibilities for individual staff members and farm (agricultural enterprise) management. For practical implementation, two basic approaches to data storage are distinguished: file systems and data platforms or repositories. The more advanced platform-based approach enables the centralisation of heterogeneous data sources, the automation of their transfer and processing, and subsequently the integration of the acquired data with analytical tools and decision-support systems. The proposed framework thus represents a path from the isolated collection of operational data to its systematic management and utilisation as a long-term information source for both operational and strategic decision-making within an agricultural enterprise.","农业日益数字化，导致农业企业产生的数据量迅速增加；然而，其实际应用往往受限于数据来源分散、数据质量参差不齐、元数据不足以及存储和管理实践不一致。本文旨在提出一个实用框架，用于农业企业数据的系统化管理，以支持其长期可用性、再利用以及融入决策过程。所提出的方法基于四个相互关联的步骤：识别数据需求与问题、创建并记录数据清单、制定数据管理计划（Data Management Plan, DMP），以及在实践中实施该计划。该框架将数据管理的技术层面——如数据格式、元数据、质量、完整性、安全性、备份与归档——与个别工作人员及农场（农业企业）管理层的责任界定联系起来。在实践实施方面，区分了两种基本的数据存储方式：文件系统和数据平台或存储库。更为先进的基于平台的方式能够实现异构数据源的集中化、其传输与处理的自动化，并随后将所获取的数据与分析工具及决策支持系统相集成。因此，所提出的框架代表了一条从孤立收集运营数据到系统化管理并将其作为农业企业内部运营和战略决策长期信息源加以利用的路径。","Agris on-line Papers in Economics and Informatics",67,{"impact":133,"substance":17,"depth":134,"authority":232,"freshness":135,"relevant":21,"comment":276},"核心期刊论文，提出农业企业数据管理四步框架，方法清晰但偏理论，对农业数据治理实践有参考价值。",[278],{"name":273,"url":270},[280,140,281,26,282],"数字农业","数据治理","农业数据管理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":286},{"candidates":287,"agricultural_evidence":46},[288,292,296],{"code":32,"score":213,"supported":46,"title":289,"abstract":290,"tags":291},[49],[],[26],{"code":157,"score":77,"supported":14,"title":293,"abstract":294,"tags":295},[],[],[280,140],{"code":297,"score":21,"supported":14,"title":298,"abstract":299,"tags":300},"I6",[],[],[281],[302,303],"农业企业 数据管理 框架","农业数据管理计划 DMP","农业企业数据管理框架-3806","10.7160\u002Faol.2026.180305",{"doi":305,"openalex_id":307,"authors":308,"venue":273,"cited_by_count":110,"oa_url":270,"card":335,"direction":116,"ingested_from":118},"W7214583099",[309,312,315,318,321,323,326,328,331,333],{"name":310,"orcid":311},"Michal Stočes","https:\u002F\u002Forcid.org\u002F0000-0001-7128-1071",{"name":313,"orcid":314},"Jan Jarolímek","https:\u002F\u002Forcid.org\u002F0000-0001-7194-3055",{"name":316,"orcid":317},"Irena Benešová","https:\u002F\u002Forcid.org\u002F0000-0002-9381-063X",{"name":319,"orcid":320},"Pavel Šimek","https:\u002F\u002Forcid.org\u002F0000-0001-9244-1476",{"name":322,"orcid":9},"Zdeňka Žáková Kroupová",{"name":324,"orcid":325},"Ludmila Pánková","https:\u002F\u002Forcid.org\u002F0000-0001-8861-4733",{"name":327,"orcid":9},"Jiří Vaněk",{"name":329,"orcid":330},"Eva Kánská","https:\u002F\u002Forcid.org\u002F0000-0001-5249-6405",{"name":332,"orcid":9},"Martin Havránek",{"name":334,"orcid":9},"Anderle Michael",{"tldr":336,"method":337,"finding":338,"direction":339,"opportunity":340},"提出农业企业数据管理框架，实现数据从孤立采集到系统化决策支持。","四步框架：识别需求、数据清单、管理计划、实施；区分文件系统与数据平台。","平台化存储可集中异构数据、自动化处理并集成决策支持系统。","数字乡村与农业信息化","可研究农业数据管理成熟度评估模型及平台落地对决策绩效的实证影响。","2026-09-29T23:31:01.874610Z",{"id":343,"title":344,"url":345,"summary":346,"summary_zh":9,"content":9,"source_name":347,"source_url":9,"published_at":348,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":349,"score_detail":350,"sources":352,"tags":354,"primary_topic":357,"secondary_topics":358,"classification":359,"search_phrases":386,"slug":389,"view_count":77,"doi":9,"paper":390,"created_at":399},3246,"Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）","https:\u002F\u002Fwww.mdpi.com\u002F3043-1204\u002F1\u002F1\u002F4","MDPI AI Precis. Agric. 1(1) 4 发表系统综述：综述2019-2026年Scopus、Web of Science及补充检索文献，考察精准农业中的应用、多模态数据集成与决策支持。综述表明研究范式从孤立的制图与预测转向集成工作流，整合卫星与无人机影像、田间传感器、气象、土壤与管理记录。报告效益包括产量预测改善、压力与病害更早检测、土壤制图更精细、灌溉与投入品更精准。然而，产量、环境和经济效益仍依赖当地条件和运行验证。核心发现是：地理参考数据本身不能确保真正的地理空间AI；可靠工作流必须解决空间依赖性、尺度不匹配、可迁移性和不确定性。可解释性和融入农场运营对可执行推荐同样重要。采用仍受数据互操作性、连接性、可负担性、隐私和技术能力限制。","MDPI AI in Precision Agriculture","2026-09-22T00:00:00Z",82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":21,"comment":351},"系统综述梳理2019-2026年GeoAI在精准农业的集成工作流与落地瓶颈，结论扎实、时效性强，对智慧农业技术路线有参考价值。",[353],{"name":347,"url":345},[140,141,355,26,356],"遥感","多模态数据",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":360},{"candidates":361,"agricultural_evidence":46},[362,367,372,378,382],{"code":157,"score":363,"supported":46,"title":364,"abstract":365,"tags":366},20,[160,161],[160],[140],{"code":32,"score":13,"supported":46,"title":368,"abstract":370,"tags":371},[369],"artificial intelligence",[],[141,26],{"code":373,"score":53,"supported":46,"title":374,"abstract":375,"tags":377},"H2",[],[376],"土壤",[],{"code":170,"score":53,"supported":46,"title":379,"abstract":380,"tags":381},[],[173],[],{"code":246,"score":21,"supported":14,"title":383,"abstract":384,"tags":385},[],[],[355],[387,388],"GeoAI 精准农业 系统综述","地理空间人工智能 精准农业","GeoAI精准农业系统综述-3246",{"doi":9,"openalex_id":9,"authors":391,"venue":9,"cited_by_count":110,"oa_url":9,"card":392,"direction":396,"ingested_from":398},[],{"tldr":393,"method":394,"finding":395,"direction":396,"opportunity":397},"系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。","系统综述Scopus、Web of Science等文献，分析多模态数据集成与决","地理参考数据不等于地理空间AI，可靠工作流须解决空间依赖、尺度、可迁移性与不确定性。","农业遥感与作物表型","可研究跨尺度空间依赖建模与可迁移性评估，提升模型在不同农场条件下的泛化能力。","agent","2026-09-23T00:04:33.172821Z",{"id":401,"title":402,"url":403,"summary":404,"summary_zh":405,"content":9,"source_name":406,"source_url":403,"published_at":407,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":408,"score_detail":409,"sources":412,"tags":414,"primary_topic":417,"secondary_topics":418,"classification":419,"search_phrases":436,"slug":439,"view_count":77,"doi":440,"paper":441,"created_at":452},2806,"Evidence-Weighted Multi-Criteria Decision Support for Subjective Quality Assessment Under Sparse and Unbalanced Information","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp16189222","Decision making for complex products often depends on subjective user experience, while competing alternatives may be supported by strongly unequal numbers of observations. This study develops an evidence-weighted multi-criteria decision-making methodology for such sparse and unbalanced information. A seven-category rating scale is aggregated at the respondent level and transformed affinely to [0, 1]. Domain-specific variance components are estimated by restricted maximum likelihood and used for empirical Bayes partial pooling, so that sparse estimates are moderated without excluding valid alternatives. AHP preference weights are kept conceptually separate from evidence weights, inherent product quality is separated from non-inherent ownership attributes, and uncertainty is propagated through Monte Carlo simulation. The empirical demonstration comprised 140 unique questionnaire records for 21 agricultural tractor brands with sample sizes from 1 to 50. Compared with direct averaging, the empirical Bayes ranking was highly preserved (Spearman ρ=0.992) while unsupported extremes were reduced. A controlled simulation showed lower RMSE for empirical Bayes than for the arithmetic mean, median, and fixed shrinkage under both Gaussian and bounded non-Gaussian data generation, with the largest benefit at n=1. Sensitivity analyses showed high ranking stability to the upper-level quality weight, perturbations of AHP weights, and bounded score transformations. The framework therefore provides reproducible uncertainty-aware decision support without treating weak evidence as either absent or equally strong as data-rich evidence.","复杂产品的决策往往依赖主观用户体验，而相互竞争的备选方案可能由数量极不均衡的观测值所支持。本研究针对此类稀疏且不均衡的信息，开发了一种证据加权多准则决策方法。将七类评分量表在受访者层面进行聚合，并通过仿射变换映射至[0, 1]区间。通过限制最大似然估计领域特定的方差分量，并将其用于经验贝叶斯部分池化，从而在不排除有效备选方案的前提下对稀疏估计进行适度调整。AHP偏好权重在概念上与证据权重保持分离，固有产品质量与非固有所有权属性相区分，不确定性通过蒙特卡洛模拟进行传播。实证演示包含21个农用拖拉机品牌的140份独立问卷记录，样本量从1到50不等。与直接平均法相比，经验贝叶斯排序结果高度保持（Spearman ρ=0.992），同时缺乏支持的极端值有所减少。一项受控模拟表明，在高斯和有界非高斯数据生成条件下，经验贝叶斯的RMSE均低于算术平均、中位数和固定收缩法，且在n=1时获益最大。敏感性分析表明，排序对上层质量权重、AHP权重的扰动以及有界得分变换均具有高度稳定性。因此，该框架提供了可重复的、考虑不确定性的决策支持，而不会将弱证据视为不存在或与数据丰富的证据同等有力。","Applied Sciences","2026-09-17T00:00:00Z",68,{"impact":135,"substance":363,"depth":410,"authority":232,"freshness":13,"relevant":21,"comment":411},17,"方法新颖、数据扎实的农机主观质量评价决策支持论文，对农业装备质量评估有参考价值，但属方法学研究，产业影响面有限。",[413],{"name":406,"url":403},[140,415,26,416],"农机装备","质量评价",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":420},{"candidates":421,"agricultural_evidence":46},[422,426,432],{"code":32,"score":133,"supported":46,"title":423,"abstract":424,"tags":425},[49],[49],[26],{"code":427,"score":45,"supported":46,"title":428,"abstract":429,"tags":431},"F5",[],[430],"tractor",[415],{"code":157,"score":21,"supported":14,"title":433,"abstract":434,"tags":435},[],[],[140],[437,438],"农机装备 决策支持 智慧农业 质量评价","农机装备 决策支持","农机装备决策支持智慧农业质量评价-2806","10.3390\u002Fapp16189222",{"doi":440,"openalex_id":442,"authors":443,"venue":406,"cited_by_count":110,"oa_url":403,"card":447,"direction":116,"ingested_from":118},"W7213447409",[444],{"name":445,"orcid":446},"K. Durczak","https:\u002F\u002Forcid.org\u002F0000-0003-4811-005X",{"tldr":448,"method":449,"finding":450,"direction":116,"opportunity":451},"提出证据加权多准则决策方法，解决稀疏不平衡主观评价下的产品排序问题。","经验贝叶斯部分池化、AHP权重分离、蒙特卡洛模拟，基于21个拖拉机品牌140份问","经验贝叶斯排序与直接平均高度一致（ρ=0.992），但能降低无支持极端值，n=1时RMSE改善最大。","可将该证据加权框架迁移到农机用户体验、智能装备评价等小样本主观决策场景，结合在线数据动态更新。","2026-09-17T23:31:04.245524Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":9,"source_name":459,"source_url":456,"published_at":460,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":464,"tags":466,"primary_topic":469,"secondary_topics":470,"classification":471,"search_phrases":488,"slug":491,"view_count":53,"doi":492,"paper":493,"created_at":516},2038,"Trustworthy AutoML indicators for climate-smart agriculture: A sustainability-governance framework for decision support","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.indic.2026.101480","Trustworthy AutoML indicators for climate-smart agriculture: A sustainability-governance framework for decision support。Environmental and Sustainability Indicators","面向气候智慧型农业的可信自动机器学习指标：面向决策支持的可持续性治理框架。《环境与可持续性指标》","Environmental and Sustainability Indicators","2026-09-09T00:00:00Z",60,{"impact":135,"substance":134,"depth":212,"authority":232,"freshness":135,"relevant":21,"comment":463},"核心期刊论文，提出面向气候智慧型农业的可信AutoML指标与可持续治理决策框架，方法有新意但属学术探讨，公共影响有限。",[465],{"name":459,"url":456},[140,141,26,467,468],"气候智慧型农业","可持续治理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":472},{"candidates":473,"agricultural_evidence":46},[474,478,483],{"code":32,"score":134,"supported":46,"title":475,"abstract":476,"tags":477},[49],[154,49],[141,26],{"code":157,"score":133,"supported":46,"title":479,"abstract":481,"tags":482},[480],"smart agriculture",[480],[140],{"code":484,"score":21,"supported":14,"title":485,"abstract":486,"tags":487},"H7",[],[],[467],[489,490],"气候智慧型农业 农业人工智能 可持续治理 决策支持","气候智慧型农业 农业人工智能","气候智慧型农业农业人工智能可持续治理决策支持-2038","10.1016\u002Fj.indic.2026.101480",{"doi":492,"openalex_id":494,"authors":495,"venue":459,"cited_by_count":110,"oa_url":456,"card":511,"direction":200,"ingested_from":118},"W7212009872",[496,499,501,503,505,508],{"name":497,"orcid":498},"Amar Singh","https:\u002F\u002Forcid.org\u002F0000-0002-5567-6521",{"name":500,"orcid":9},"Sukhbir Singh Walia",{"name":502,"orcid":9},"Ajay Singh",{"name":504,"orcid":9},"Shakti Kumar",{"name":506,"orcid":507},"Sahraoui Dhelim","https:\u002F\u002Forcid.org\u002F0000-0002-3620-1395",{"name":509,"orcid":510},"Chatter Singh","https:\u002F\u002Forcid.org\u002F0009-0005-6154-9237",{"tldr":512,"method":513,"finding":514,"direction":116,"opportunity":515},"提出面向气候智慧型农业的可信AutoML指标体系与可持续治理框架，用于决策支持。","构建可信AutoML指标与可持续治理框架，结合环境可持续性指标。","可信AutoML指标可支撑气候智慧型农业的可持续治理决策。","可探索可信AutoML指标在农业碳核算与气候适应决策中的实证验证与落地。","2026-09-10T23:30:09.176914Z"]