[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2789":3,"related-2789":87},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":86},2789,"Spatial Modeling of Soil Functions and Environmental Vulnerability Using Multi-source Geospatial Data in the Bhavani River Basin, South India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02575-y","Spatial Modeling of Soil Functions and Environmental Vulnerability Using Multi-source Geospatial Data in the Bhavani River Basin, South India。Journal of the Indian Society of Remote Sensing","印度南部巴瓦尼河流域基于多源地理空间数据的土壤功能与环境脆弱性空间建模。《印度遥感学会杂志》",null,"Journal of the Indian Society of Remote Sensing","2026-09-16T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,16,15,13,9,1,"印度流域尺度的多源遥感土壤功能与脆弱性建模研究，方法有参考价值但属区域案例，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","遥感","土壤功能","环境脆弱性","空间建模",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":60},[39,48,55],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"H2",14,true,[44],"soil",[46,44],"土壤",[],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":54},"I3",7,[],[28,53],"remote sensing",[28],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"I6",[],[],[27],"农业相关性需要复核",[62,63],"环境脆弱性 土壤功能 数字乡村 空间建模","环境脆弱性 土壤功能","环境脆弱性土壤功能数字乡村空间建模-2789",0,"10.1007\u002Fs12524-026-02575-y",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":65,"oa_url":9,"card":79,"direction":83,"ingested_from":85},"W7213282964",[70,72,74,76],{"name":71,"orcid":9},"S. Zuvairiya",{"name":73,"orcid":9},"S. R. Singarasubramanian",{"name":75,"orcid":9},"A. Bobby Jones",{"name":77,"orcid":78},"V. Gopal","https:\u002F\u002Forcid.org\u002F0000-0002-5498-3183",{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"基于多源地理空间数据对印度巴瓦尼河流域土壤功能与环境脆弱性进行空间建模。","多源地理空间数据与空间建模方法。","揭示了该流域土壤功能与环境脆弱性的空间分布格局。","农业遥感与作物表型","可引入机器学习与高分辨率遥感，提升土壤功能与脆弱性制图精度并拓展至其他流域。","openalex","2026-09-17T23:30:34.771157Z",{"total":88,"page":22,"page_size":88,"items":89},6,[90,176,268,329,392,453],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":98,"score_detail":99,"sources":104,"tags":106,"primary_topic":110,"secondary_topics":115,"classification":116,"search_phrases":154,"slug":157,"view_count":65,"doi":158,"paper":159,"created_at":175},5395,"Strengthening Flood Resilience in Data-Sparse Agricultural Regions: A Planning Approach for Coastal Guyana","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23451582","Abstract. Coastal Guyana concentrates most of the country’s population and agricultural production in low-lying polder landscapes that are increasingly exposed to flooding from intense rainfall, sea-level rise, and deterioration of drainage and irrigation infrastructure. Engineering design and resilience planning in these settings are often constrained by limited hydrometric monitoring (discharge, water levels), incomplete documented drainage network, and restricted access to institutional datasets. This paper presents the value of using a low-information framework for flood resilience planning and preliminary hydraulic design in data-sparse agricultural regions, using the 364 km² part of Guyana’s Mahaica–Mahaicony catchment as a case study. The framework integrates (i) rainfall frequency analysis from long-term gauge records, (ii) remote sensing and global datasets (DEM and land-use\u002Fland-cover) to delineate sub-basins and screen flood impact, (iii) simplified runoff and volume-based drainage calculations consistent with rice-field storage and saturated soils, and (iv) structured stakeholder engagement to identify flood-prone areas, operational constraints, and feasible interventions. Six-sub-basins are used for developing a routing strategy to distribute runoff volumes and to explicitly incorporate upstream detention in the wetlands, thereby reducing peak flows conveyed northward through constrained canal corridors. High-level drainage canals, dedicated conveyance paths for critical sub-basins, and dual-purpose drains supporting both drainage and irrigation storage are used to determine preliminary cross-section designs, which are then assessed with 1D hydraulic modelling under tidal boundary conditions. The approach supports phased investment planning that improves flood safety, protects livelihoods, and increases agricultural reliability while aligning with national adaptation priorities.","摘要：圭亚那沿海地区集中了全国大部分人口和农业生产，地处低洼圩田景观，日益面临强降雨、海平面上升以及排水灌溉基础设施老化所带来的洪涝风险。在此类地区，工程设计与韧性规划往往受限于水文监测（流量、水位）资料不足、排水管网记录不完整以及机构数据集获取受限。本文以圭亚那马海卡—马海科尼（Mahaica–Mahaicony）流域364 km²的区域为案例，展示了在数据稀疏的农业地区采用低信息框架进行洪涝韧性规划与初步水力设计的价值。该框架整合了：(i) 基于长期测站记录的降雨频率分析；(ii) 利用遥感与全球数据集（DEM及土地利用\u002F土地覆盖）划分子流域并筛查洪涝影响；(iii) 与稻田蓄水和饱和土壤条件相适应的简化径流与基于水量的排水计算；(iv) 结构化利益相关方参与，以识别易涝区域、运行约束和可行干预措施。研究将流域划分为六个子流域，用于制定汇流路径策略，以分配径流量并明确纳入上游湿地滞蓄，从而削减通过受限渠廊向北输送的峰值流量。基于高位排水渠、关键子流域专用输水通道以及兼顾排水与灌溉蓄水的两用沟渠，确定初步断面设计，并在潮汐边界条件下采用一维水动力模型进行评估。该方法支持分阶段投资规划，在提升防洪安全、保护生计和提高农业可靠性的同时，与国家适应优先事项相衔接。","Geo-Eco-Marina","2026-10-18T00:00:00Z",70,{"impact":100,"substance":101,"depth":102,"authority":20,"freshness":17,"relevant":22,"comment":103},12,20,17,"该论文提出一种低信息条件下的防洪韧性规划框架，方法新颖，但案例位于圭亚那，对我国三农信息化的直接参考价值有限，因此不宜进入每日精选。",[105],{"name":96,"url":93},[27,28,107,108,109],"气候适应","防洪韧性","灌排设施",{"code":49,"slug":111,"name":112,"path":113,"parent_code":114},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":117,"locked":14,"version":35,"method":36,"evidence":118},"classified",{"candidates":119,"agricultural_evidence":42},[120,124,131,138,145,150],{"code":49,"score":50,"supported":42,"title":121,"abstract":122,"tags":123},[],[28,53],[28],{"code":125,"score":88,"supported":42,"title":126,"abstract":127,"tags":130},"A1",[],[128,129],"稻田","rice",[],{"code":132,"score":88,"supported":42,"title":133,"abstract":134,"tags":137},"H3",[],[135,136],"灌溉","irrigation",[],{"code":139,"score":140,"supported":42,"title":141,"abstract":142,"tags":144},"H7",4,[],[143],"洪涝",[107],{"code":40,"score":146,"supported":42,"title":147,"abstract":148,"tags":149},3,[],[46],[],{"code":56,"score":22,"supported":14,"title":151,"abstract":152,"tags":153},[],[],[27],[155,156],"Guyana Mahaica Mahaicony 洪水","沿海稻田 排水规划","GuyanaMahaicaMahaicony洪水-5395","10.5281\u002Fzenodo.23451582",{"doi":158,"openalex_id":160,"authors":161,"venue":96,"cited_by_count":65,"oa_url":93,"card":169,"direction":83,"ingested_from":85},"W7222952224",[162,164,166],{"name":163,"orcid":9},"Omesh Persaud",{"name":165,"orcid":9},"Colis Allen",{"name":167,"orcid":168},"Ioana Popescu","https:\u002F\u002Forcid.org\u002F0000-0002-1009-5424",{"tldr":170,"method":171,"finding":172,"direction":173,"opportunity":174},"提出低信息框架，为数据稀缺农业区规划洪水韧性并做初步水力设计。","降雨频率分析、遥感DEM与土地利用、简化径流计算、利益相关者参与及一维水力模型。","上游湿地滞蓄与分区排水可降低峰值流量，支撑分阶段投资规划。","农业绿色发展与碳","可探索低监测区遥感与参与式数据融合的洪水韧性规划方法及作物淹水风险量化。","2026-10-20T23:30:19.345049Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":183,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":184,"score_detail":185,"sources":189,"tags":191,"primary_topic":195,"secondary_topics":200,"classification":201,"search_phrases":239,"slug":242,"view_count":243,"doi":244,"paper":245,"created_at":267},4211,"An integrated scientometric and BERTopic-based analysis on examining the role of artificial intelligence in achieving Sustainable Development Goals (SDGs)","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04841-y","This study investigates the role of Artificial Intelligence (AI) in achieving the United Nations Sustainable Development Goals (SDGs) through a comprehensive scientometric analysis. It explores the evolution of scholarly research, identifies leading contributors, maps thematic trends, examines how AI technologies contribute to advancing sustainability across multiple domains, and identifies latent semantic themes. A total of 3262 unique scholarly records published between 1989 and 2025, after deduplication and screening, were finalized from the Scopus and Web of Science databases. The dataset was analysed using Biblioshiny (R-based Bibliometrix) and VOSviewer to examine publication trends, citation impact, collaborative networks, thematic evolution, and conceptual structures. Indicators such as Relative Growth Rate (RGR), Doubling Time (DT), h-index, g-index, and Lotka’s Law were applied to evaluate research productivity and scholarly influence. In addition, BERTopic-based abstract-level topic modelling was applied to identify latent semantic themes, SDG-linked thematic domains, temporal evolution and potential research gaps. Results reveal a significant growth in AI-SDG research, particularly after 2015, with an accelerated increase post-2020. India, USA, and China emerged as the most productive contributors, while countries like USA, India, United Kingdom and Australia lead in research impact and citation quality. Thematic evolution analysis reveals a transition from foundational themes such as AI, machine learning, climate change, and decision support systems toward more integrated and sustainability-oriented research themes, including sustainable development and SDGs. Thematic linkages further demonstrate the persistent foundational role of AI and machine learning, alongside the growing centrality of climate change and sustainable development, reflecting the increasing convergence of AI research with global sustainability priorities. BERTopic analysis further revealed eleven interpretable abstract-level thematic domains after excluding one heterogeneous outlier category. The dominant themes included digital transformation and green innovation, AI-enabled education, digital health, smart agriculture, renewable energy systems, clean water and sanitation, remote sensing, NLP-based decision support, sustainable construction, tourism, and sustainable chemistry\u002Fpharmaceutical applications. This study maps the AI–SDG research landscape by integrating bibliometric indicators with thematic evolution, conceptual analysis, and abstract-level semantic topic modelling. The findings provide insights into the development and thematic orientation of AI-related research in the context of sustainable development. The use of BERTopic complements conventional bibliometric approaches by examining semantic patterns within publication abstracts alongside keyword and citation-based analyses.","本研究通过一项全面的科学计量分析，探讨了人工智能（Artificial Intelligence, AI）在实现联合国可持续发展目标（Sustainable Development Goals, SDGs）中的作用。研究考察了学术研究的演进历程，识别了主要贡献者，绘制了主题趋势图谱，分析了AI技术如何在多个领域推动可持续发展，并识别了潜在语义主题。经去重和筛选后，最终从Scopus和Web of Science数据库中确定了1989年至2025年间发表的3262条独特学术记录。使用Biblioshiny（基于R的Bibliometrix）和VOSviewer对数据集进行分析，以考察出版趋势、引用影响、合作网络、主题演进和概念结构。采用相对增长率（Relative Growth Rate, RGR）、倍增时间（Doubling Time, DT）、h指数、g指数和洛特卡定律（Lotka's Law）等指标评估研究生产力和学术影响力。此外，应用基于BERTopic的摘要级主题建模，以识别潜在语义主题、与SDG相关的主题领域、时间演变和潜在研究空白。结果显示，AI-SDG研究显著增长，尤其在2015年之后，2020年后增长加速。印度、美国和中国是最多产的贡献者，而美国、印度、英国和澳大利亚等国家在研究影响力和引用质量方面领先。主题演进分析揭示了从AI、机器学习、气候变化和决策支持系统等基础主题向更综合、面向可持续发展的研究主题（包括可持续发展和SDGs）的转变。主题关联进一步表明AI和机器学习的持续基础性作用，同时气候变化和可持续发展的中心性日益增强，反映了AI研究与全球可持续发展优先事项日益融合。BERTopic分析在排除一个异质性离群类别后，进一步揭示了十一个可解释的摘要级主题领域。主导主题包括数字化转型与绿色创新、AI赋能教育、数字健康、智慧农业、可再生能源系统、清洁水和卫生设施、遥感、基于自然语言处理的决策支持、可持续建筑、旅游业以及可持续化学\u002F制药应用。本研究通过整合文献计量","Discover Sustainability","2026-10-04T00:00:00Z",80,{"impact":186,"substance":187,"depth":186,"authority":20,"freshness":21,"relevant":22,"comment":188},18,22,"该研究以3262篇文献的计量与BERTopic主题建模系统梳理AI支撑SDGs的研究格局，其中智慧农业、遥感、数字健康等主题与农业信息化高度相关，方法新颖、数据规模大，值得进入每日精选。",[190],{"name":182,"url":179},[27,192,193,194,28],"智慧农业","农业人工智能","可持续发展",{"code":196,"slug":197,"name":198,"path":199,"parent_code":114},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":117,"locked":14,"version":35,"method":36,"evidence":202},{"candidates":203,"agricultural_evidence":42},[204,212,218,222,228,235],{"code":196,"score":186,"supported":42,"title":205,"abstract":207,"tags":211},[206],"artificial intelligence",[208,209,206,210],"机器学习","machine learning","decision support",[193],{"code":213,"score":50,"supported":42,"title":214,"abstract":215,"tags":217},"I1",[],[192,216],"smart agriculture",[192],{"code":49,"score":50,"supported":42,"title":219,"abstract":220,"tags":221},[],[28,53],[28],{"code":139,"score":88,"supported":42,"title":223,"abstract":224,"tags":227},[],[225,226],"气候变化","climate change",[],{"code":229,"score":88,"supported":42,"title":230,"abstract":231,"tags":234},"I7",[],[232,233],"文献计量","bibliometric",[],{"code":56,"score":22,"supported":14,"title":236,"abstract":237,"tags":238},[],[],[27],[240,241],"AI SDG 文献计量","BERTopic 可持续农业","AISDG文献计量-4211",2,"10.1007\u002Fs43621-026-04841-y",{"doi":244,"openalex_id":246,"authors":247,"venue":182,"cited_by_count":65,"oa_url":179,"card":260,"direction":266,"ingested_from":85},"W7219683711",[248,251,254,257],{"name":249,"orcid":250},"Reetu Verma","https:\u002F\u002Forcid.org\u002F0000-0002-0567-7219",{"name":252,"orcid":253},"Ashok Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-3279-2895",{"name":255,"orcid":256},"Kavita Pabreja","https:\u002F\u002Forcid.org\u002F0000-0001-9856-0900",{"name":258,"orcid":259},"Saurabh Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-5924-6425",{"tldr":261,"method":262,"finding":263,"direction":264,"opportunity":265},"用科学计量与BERTopic分析3262篇文献，揭示AI助力可持续发展目标的研究格局与主题演化。","Scopus\u002FWoS文献，Biblioshiny、VOSviewer计量分析加B","AI-SDG研究2015年后激增，主题从基础AI转向可持续发展，识别出智慧农业等11个主题域。","农业人工智能与决策模型","可聚焦智慧农业主题，探究AI在农业SDG中的具体贡献路径与区域差异，填补细粒度实证空白。","智慧农业 \u002F 农业物联网","2026-10-05T23:30:09.006483Z",{"id":269,"title":270,"url":271,"summary":272,"summary_zh":273,"content":9,"source_name":274,"source_url":271,"published_at":275,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":276,"score_detail":277,"sources":279,"tags":281,"primary_topic":285,"secondary_topics":286,"classification":292,"search_phrases":312,"slug":315,"view_count":243,"doi":316,"paper":317,"created_at":328},4102,"Application of GIS and Remote Sensing Techniques in Geospatial Mapping of Gully Statuses of an Alfisols in Yola South Local Government Area, Adamawa State","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v10.no1.2026.pg155.165","This research work saddled to study the application of GIS and Remote Sensing (RS) Techniques in geospatial mapping of gully erosion statuses of an Alfisols in Yola South LGA. Bole farmland was selected where expansion and development of gully was geospatially mapped out for the period of 20 years (2004-2024) at interval of five (5) years using GIS and RS techniques. The results reveal a concerning trend of increasing gully erosion, with significant growth in affected areas over the years. The highest percentage of increase occurred at 2014-2019 with about 58,069.00 m2 (65.5 %) and in 2019-2024was increased with 14,154.00 m2 (15.97 %). The total area affected in the area within the study period was 88,627.00 m2 caused by heavy rainfall and deforestation. Therefore, there are needs for effective land management practices, policy interventions, and community engagement to mitigate the effects of gully erosion.","本研究旨在探讨GIS与遥感（RS）技术在约拉南部地方政府区淋溶土冲沟侵蚀状况地理空间制图中的应用。选取博莱农田为研究区，利用GIS与遥感技术，以5年为间隔，对2004—2024年共20年间冲沟的扩展与发育进行了地理空间制图。结果表明，冲沟侵蚀呈令人担忧的加剧趋势，受影响面积逐年显著增长。增幅最大的时段为2014—2019年，约增加58 069.00 m²（65.5%）；2019—2024年增加14 154.00 m²（15.97%）。研究期内该区域受影响总面积为88 627.00 m²，主要由强降雨和森林砍伐所致。因此，需要采取有效的土地管理措施、政策干预和社区参与，以减轻冲沟侵蚀的影响。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-09-30T00:00:00Z",54,{"impact":17,"substance":186,"depth":41,"authority":88,"freshness":17,"relevant":22,"comment":278},"基于GIS与遥感的20年沟蚀动态监测，数据具体、结论明确，但属区域案例研究，公共影响有限。",[280],{"name":274,"url":271},[27,28,282,283,284],"土壤侵蚀","GIS","水土保持",{"code":49,"slug":111,"name":112,"path":113,"parent_code":114},[287],{"code":288,"slug":289,"name":290,"path":291,"parent_code":114},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":117,"locked":14,"version":35,"method":36,"evidence":293},{"candidates":294,"agricultural_evidence":42},[295,299,303,308],{"code":49,"score":19,"supported":42,"title":296,"abstract":297,"tags":298},[53],[28,53],[28],{"code":288,"score":100,"supported":42,"title":300,"abstract":301,"tags":302},[283],[283],[283],{"code":304,"score":22,"supported":14,"title":305,"abstract":306,"tags":307},"H6",[],[],[284],{"code":56,"score":22,"supported":14,"title":309,"abstract":310,"tags":311},[],[],[27],[313,314],"Yola South 沟蚀 遥感","Alfisols 沟蚀 GIS 制图","YolaSouth沟蚀遥感-4102","10.56201\u002Fwjimt.v10.no1.2026.pg155.165",{"doi":316,"openalex_id":318,"authors":319,"venue":274,"cited_by_count":65,"oa_url":322,"card":323,"direction":83,"ingested_from":85},"W7214920253",[320],{"name":321,"orcid":9},"Mohammed Pakra Umar","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 10 NO. 1 2026\u002FAPPLICATION OF GIS AND REMOTE 156-165.pdf",{"tldr":324,"method":325,"finding":326,"direction":83,"opportunity":327},"用GIS和遥感技术对尼日利亚Yola South地区20年沟蚀动态进行空间制图与监测。","基于GIS与遥感影像，按5年间隔提取2004-2024年沟蚀范围并计算面积变化。","沟蚀面积持续扩大，2014-2019年增幅最大达65.5%，20年累计影响88,627平方米。","可结合多源高分辨率遥感与机器学习，构建沟蚀风险预警模型并评估保护性耕作等治理措施效果。","2026-10-03T23:30:37.949646Z",{"id":330,"title":331,"url":332,"summary":333,"summary_zh":334,"content":9,"source_name":335,"source_url":332,"published_at":336,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":337,"score_detail":338,"sources":340,"tags":342,"primary_topic":346,"secondary_topics":347,"classification":348,"search_phrases":364,"slug":367,"view_count":140,"doi":368,"paper":369,"created_at":391},3492,"Compliance Algorithm for Socio-Environmental Safeguards in Legal Amazon: A Traceability and Risk Assessment Model for Agricultural Commodities Chain under Regulation (EU) 2023\u002F1115","https:\u002F\u002Fdoi.org\u002F10.66104\u002F5gcafj90","Regulation (EU) 2023\u002F1115 requires agricultural commodities placed on the European market to be deforestation-free from 31 December 2020, produced in line with laws in its country of origin, and covered by a due diligence statement identifying production plots by geolocation. Demonstrating compliance depends on a unit of analysis—rural property and plot—that falls between municipal-scale trade-flow models and pixel-scale remote sensing products, while relying on heterogeneous and consistently incomplete databases. This article presents COMPLY-SOY-MA, a model for assessing traceability, risk, and socio-environmental compliance across agricultural commodities supply chains in Legal Amazon. Its main contribution is a credibility estimator that separates two often-confused dimensions: substantive compliance, measured through a composite index of thirty-one indicators aggregated using power means with controlled compensability, and supporting-evidence quality, measured through a five-indicator index. Risk is estimated by shrinking observed deficiency toward a territorial prior, weighted by the power of the evidence index, so opaque units are not assessed using averages from units that disclose information. Under isolated changes in evidence, providing more evidence reduces risk only for genuinely compliant units. Traceability is modelled as a mass-flow-weighted multigraph, using origin entropy and a formal distinction between exposure and contamination index. Scope is stated clearly: the study delivers a formal model and reference implementation, with results based on mathematical derivation and domain characterization. It specifies but does not execute a validation protocol, and its risk score is not a calibrated probability.","欧盟第2023\u002F1115号条例要求，投放欧洲市场的农产品自2020年12月31日起不得涉及毁林，须依照原产国法律生产，并须通过尽职调查声明予以涵盖，该声明需以地理定位方式标明生产地块。证明合规取决于一个分析单元——农村地产与地块——它介于市级贸易流模型与像素级遥感产品之间，同时依赖于异质且始终不完整的数据库。本文提出COMPLY-SOY-MA，一个用于评估合法亚马逊地区农产品供应链可追溯性、风险及社会环境合规性的模型。其主要贡献在于一个可信度估计器，该估计器区分了两个常被混淆的维度：实质性合规，通过由三十一项指标构成的综合指数衡量，该指数采用可控补偿性的幂均值进行聚合；以及支撑性证据质量，通过一个五项指标指数衡量。风险估计通过将观测到的缺陷向地域先验收缩来实现，并以证据指数的幂进行加权，从而避免对不透明单元使用披露信息单元的平均值进行评估。在证据发生孤立变化的情况下，提供更多证据仅对真正合规的单元降低风险。可追溯性被建模为质量流加权的多重图，使用来源熵以及暴露指数与污染指数之间的形式化区分。研究范围已明确界定：本研究提供一个形式化模型及参考实现，其结果基于数学推导与领域刻画。它规定但并未执行验证方案，其风险评分并非经过校准的概率。","Revista Multidisciplinar do Nordeste Mineiro","2026-09-24T00:00:00Z",63,{"impact":17,"substance":187,"depth":186,"authority":88,"freshness":21,"relevant":22,"comment":339},"提出面向欧盟零毁林法规的农产品供应链可追溯与风险评估模型，方法新颖但仅属形式化模型、未经验证，且来源为地方性期刊，影响力有限。",[341],{"name":335,"url":332},[27,343,344,28,345],"农业供应链","农产品溯源","欧盟法规",{"code":49,"slug":111,"name":112,"path":113,"parent_code":114},[],{"status":117,"locked":14,"version":35,"method":36,"evidence":349},{"candidates":350,"agricultural_evidence":42},[351,355,360],{"code":49,"score":50,"supported":42,"title":352,"abstract":353,"tags":354},[],[28,53],[28],{"code":356,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},"E4",[],[],[344],{"code":56,"score":22,"supported":14,"title":361,"abstract":362,"tags":363},[],[],[27],[365,366],"EU 2023 1115 农产品 溯源","Legal Amazon 大豆 供应链","EU20231115农产品溯源-3492","10.66104\u002F5gcafj90",{"doi":368,"openalex_id":370,"authors":371,"venue":335,"cited_by_count":22,"oa_url":332,"card":386,"direction":83,"ingested_from":85},"W7214225231",[372,375,377,379,381,384],{"name":373,"orcid":374},"Felipe Oliveira Carvalho","https:\u002F\u002Forcid.org\u002F0000-0002-7540-286X",{"name":376,"orcid":9},"Allan Kardec Duailibe Barros Filho",{"name":378,"orcid":9},"José Artur Lima Cabral Marques",{"name":380,"orcid":9},"Rosângela Maria Guimarães Rosa",{"name":382,"orcid":383},"Paulo Fortes Neto","https:\u002F\u002Forcid.org\u002F0000-0001-5837-8450",{"name":385,"orcid":9},"Silvio Carlos Leite Mesquita",{"tldr":387,"method":388,"finding":389,"direction":173,"opportunity":390},"提出COMPLY-SOY-MA模型，评估合法亚马逊农产品供应链的可追溯性、风险与社会环境合规性。","构建可信度估计器，用31项指标复合指数与证据质量指数，结合质量流多重图与熵。","区分实质合规与证据质量，证据增加仅降低真正合规单元的风险，避免不透明单元被平均化。","可延伸至多品类供应链验证，并开发实证校准的风险概率模型与验证协议。","2026-09-25T23:30:30.374587Z",{"id":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":275,"category":12,"cover_url":9,"hotness":399,"is_selected":14,"score":400,"score_detail":401,"sources":403,"tags":407,"primary_topic":408,"secondary_topics":409,"classification":410,"search_phrases":437,"slug":440,"view_count":140,"doi":441,"paper":442,"created_at":452},3462,"AI For Sustainable Development Opportunities & Innovation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933761","Abstract Artificial Intelligence is increasingly being explored as a tool for accelerating progress toward sustainable development. AI can analyse large datasets, identify patterns, forecast events, optimise systems and support decision-making across agriculture, energy, water, healthcare, education, cities, industry and environmental management. Recent research shows that AI-for-SDG research is expanding rapidly, while also revealing gaps in social inclusion, governance and assessment of AI’s own environmental footprint. This project examines the major opportunities for AI-enabled sustainable development, including smart agriculture, renewable-energy optimisation, climate and disaster forecasting, intelligent waste management, sustainable cities, healthcare and education. It also discusses innovation pathways such as machine learning, computer vision, remote sensing, generative AI, digital twins and edge AI. The study emphasises that technological capability alone is insufficient: responsible AI requires reliable data, transparency, privacy, human oversight, equitable access, energy-efficient computing and lifecycle environmental assessment.","摘要 人工智能正日益被视为加速可持续发展进程的工具。人工智能可以分析大型数据集、识别模式、预测事件、优化系统，并在农业、能源、水资源、医疗、教育、城市、工业与环境管理等领域支持决策。近期研究表明，人工智能促进可持续发展目标（SDG）的研究正在迅速扩展，同时也揭示了在社会包容、治理以及人工智能自身环境足迹评估方面的不足。本项目考察了人工智能赋能可持续发展的主要机遇，包括智慧农业、可再生能源优化、气候与灾害预测、智能废物管理、可持续城市、医疗和教育。项目还讨论了机器学习、计算机视觉、遥感、生成式人工智能、数字孪生和边缘人工智能等创新路径。研究强调，仅靠技术能力是不够的：负责任的人工智能需要可靠的数据、透明度、隐私保护、人类监督、公平获取、节能计算以及生命周期环境评估。","Zenodo (CERN European Organization for Nuclear Research)",25,68,{"impact":186,"substance":18,"depth":19,"authority":20,"freshness":88,"relevant":22,"comment":402},"系统梳理AI赋能农业等可持续发展领域的机遇与治理挑战，属综合性研究综述，对智慧农业方向有参考价值但非突破性成果。",[404,405],{"name":398,"url":395},{"name":398,"url":406},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[27,192,193,194,28],{"code":196,"slug":197,"name":198,"path":199,"parent_code":114},[],{"status":117,"locked":14,"version":35,"method":36,"evidence":411},{"candidates":412,"agricultural_evidence":42},[413,418,424,428,433],{"code":213,"score":13,"supported":42,"title":414,"abstract":415,"tags":417},[],[192,416,216],"数字孪生",[192],{"code":196,"score":13,"supported":42,"title":419,"abstract":420,"tags":423},[],[208,421,209,206,422],"计算机视觉","computer vision",[193],{"code":49,"score":50,"supported":42,"title":425,"abstract":426,"tags":427},[],[28,53],[28],{"code":132,"score":146,"supported":42,"title":429,"abstract":430,"tags":432},[],[431],"水资源",[],{"code":56,"score":22,"supported":14,"title":434,"abstract":435,"tags":436},[],[],[27],[438,439],"AI 可持续发展 智慧农业","农业人工智能 可持续发展 数字乡村 智慧农业","AI可持续发展智慧农业-3462","10.5281\u002Fzenodo.22933761",{"doi":441,"openalex_id":443,"authors":444,"venue":398,"cited_by_count":65,"oa_url":395,"card":447,"direction":266,"ingested_from":85},"W7214172367",[445],{"name":446,"orcid":9},"Saloni Ananda Patil",{"tldr":448,"method":449,"finding":450,"direction":173,"opportunity":451},"综述AI在可持续发展各领域的机会与创新路径，并强调负责任AI的治理要求。","文献综述，覆盖机器学习、计算机视觉、遥感、数字孪生与边缘AI等。","AI-for-SDG研究快速扩张，但社会包容、治理与AI自身环境足迹评估仍存缺口。","可量化AI自身能耗与碳足迹，并评估其在农业减排中的净环境效益。","2026-09-25T23:30:08.894307Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":9,"source_name":459,"source_url":456,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":460,"score_detail":461,"sources":463,"tags":465,"primary_topic":469,"secondary_topics":474,"classification":475,"search_phrases":513,"slug":516,"view_count":22,"doi":517,"paper":518,"created_at":539},2679,"Space Science and Technology for Sustainable Development and Climate Resilience in Africa: A Review of Emerging Applications and Challenges","https:\u002F\u002Fdoi.org\u002F10.62292\u002Fnjp.v35(s).2026.731","This study undertakes a systematic review to examine the role of space science and technology in addressing Africa’s pressing challenges in agriculture, water management, disaster resilience, urbanization, and digital inclusion. Evaluating how Earth Observation, satellite navigation, and communication technologies contribute to achieving the Sustainable Development Goals (SDGs) being its main objective. The review draws on secondary sources, including peer‑reviewed journals, space agency reports, and global datasets, with content analysis employed to identify thematic applications and emerging trends. The analysis highlights major themes: precision agriculture, groundwater mapping, urban growth monitoring, disaster early warning systems, climate adaptation, natural resource conservation, and digital inclusion. Case studies from Kenya, Nigeria, Ethiopia, Rwanda, and Mozambique demonstrate measurable benefits, including strengthened food security, improved water resource management, guided infrastructure planning, and enhanced resilience to climate‑induced hazards. Institutional initiatives by the National Space Research and Development Agency (NASRDA) in Nigeria and the African Space Agency further illustrate Africa’s growing commitment to harnessing space technologies for socio‑economic transformation. Despite these advances, more still needs to be done to meet the international standard. Persistent barriers affecting Africa include limited funding, dependence on external datasets, and shortages of technical expertise, which constrain the scalability and sustainability of interventions. Future directions should prioritize investment in indigenous satellite missions, expansion of STEM education, and regional cooperation to build capacity and ensure data sovereignty. Overall, the review underscores that space science offers Africa a strategic pathway to resilience, innovation, and inclusive growth, positioning the continent to accelerate progress toward the SDGs and secure a sustainable future.","本研究开展系统性综述，考察空间科学技术在应对非洲农业、水资源管理、灾害韧性、城市化及数字包容等紧迫挑战中的作用。其主要目标是评估地球观测、卫星导航与通信技术如何助力实现可持续发展目标（SDGs）。本综述基于二手资料，包括同行评审期刊、航天机构报告及全球数据集，并采用内容分析法识别主题性应用与新兴趋势。分析凸显了若干主要主题：精准农业、地下水测绘、城市扩张监测、灾害预警系统、气候适应、自然资源保护及数字包容。来自肯尼亚、尼日利亚、埃塞俄比亚、卢旺达和莫桑比克的案例研究展示了可衡量的效益，包括增强粮食安全、改善水资源管理、指导基础设施规划以及提升对气候诱发灾害的韧性。尼日利亚国家空间研究与发展局（NASRDA）及非洲航天局的机构性举措进一步表明，非洲日益致力于利用空间技术推动社会经济转型。尽管取得了这些进展，但要达到国际标准仍需付出更多努力。影响非洲的持续障碍包括资金有限、依赖外部数据集以及技术专长短缺，这些因素制约了干预措施的可扩展性与可持续性。未来方向应优先投资于本土卫星任务、扩大STEM教育以及区域合作，以建设能力并确保数据主权。总体而言，本综述强调，空间科学为非洲提供了一条通往韧性、创新与包容性增长的战略路径，使该大陆能够加速实现可持续发展目标的进程并确保可持续的未来。","Nigerian Journal of Physics",78,{"impact":186,"substance":101,"depth":102,"authority":20,"freshness":13,"relevant":22,"comment":462},"系统综述空间科技在非洲农业、水资源与灾害韧性中的应用与瓶颈，含多国案例与机构实践，对农业遥感与数字乡村议题有参考价值，但属域外经验、非国内政策或产业突破。",[464],{"name":459,"url":456},[27,466,467,28,468],"精准农业","非洲农业","气候韧性",{"code":139,"slug":470,"name":471,"path":472,"parent_code":473},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":117,"locked":14,"version":35,"method":36,"evidence":476},{"candidates":477,"agricultural_evidence":42},[478,483,488,495,500,505,509],{"code":139,"score":100,"supported":42,"title":479,"abstract":481,"tags":482},[480],"climate resilience",[107],[468],{"code":213,"score":50,"supported":42,"title":484,"abstract":485,"tags":487},[],[466,486],"precision agriculture",[466],{"code":489,"score":88,"supported":42,"title":490,"abstract":491,"tags":494},"A8",[],[492,493],"粮食安全","food security",[],{"code":132,"score":88,"supported":42,"title":496,"abstract":497,"tags":499},[],[431,498],"water resource",[],{"code":501,"score":140,"supported":42,"title":502,"abstract":503,"tags":504},"J3",[],[467],[467],{"code":49,"score":22,"supported":14,"title":506,"abstract":507,"tags":508},[],[],[28],{"code":56,"score":22,"supported":14,"title":510,"abstract":511,"tags":512},[],[],[27],[514,515],"数字乡村 气候韧性 精准农业 非洲农业","数字乡村 气候韧性","数字乡村气候韧性精准农业非洲农业-2679","10.62292\u002Fnjp.v35(s).2026.731",{"doi":517,"openalex_id":519,"authors":520,"venue":459,"cited_by_count":65,"oa_url":456,"card":533,"direction":538,"ingested_from":85},"W7213235414",[521,523,525,527,529,531],{"name":522,"orcid":9},"Rufus Oladosu Olakunle",{"name":524,"orcid":9},"Abdulrahman Adegbite",{"name":526,"orcid":9},"Harry Obimba",{"name":528,"orcid":9},"Tosin Awotoye",{"name":530,"orcid":9},"Linda Abegunde",{"name":532,"orcid":9},"Jamiu Taiwo Aileru",{"tldr":534,"method":535,"finding":536,"direction":83,"opportunity":537},"系统综述空间科技在非洲农业、水资源、减灾等领域应用及挑战。","系统综述二手文献与机构报告，内容分析提炼主题与案例。","空间科技显著提升非洲粮食安全与水管理，但受资金、数据依赖和人才短缺制约。","可研究非洲本土卫星数据主权与低成本遥感在精准农业中的适配机制。","数字乡村与农业信息化","2026-09-16T23:30:36.955820Z"]