{"id":"W4412314677","doi":"","title":"Multi-platform remote sensing of nitrogen status and leaching from agricultural fields with random forest regression approach","year":2022,"lang":"en","type":"article","venue":"","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Social Fund; Agencia Estatal de Investigación; European Regional Development Fund; Fundação para a Ciência e a Tecnologia; Leibniz-Gemeinschaft; Natural Environment Research Council; Biotechnology and Biological Sciences Research Council; Agriculture and Agri-Food Canada; Centre for Water Technology, Aarhus University; Directorate for Biological Sciences; Thünen-Institut; Cotton Research and Development Corporation; Tempus Közalapítvány; Landwirtschaftliche Rentenbank; Umweltbundesamt; Research Institute for Humanity and Nature; Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas; Alexander von Humboldt-Stiftung; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Ministerio para la Transición Ecológica y el Reto Demográfico; Deutsche Forschungsgemeinschaft; Ministerial Standing Committee on Scientific and Technological Cooperation of the Organization of Islamic Cooperation; Bundesministerium für Ernährung und Landwirtschaft; Ministarstvo znanosti i obrazovanja; UK Research and Innovation; Universidad Politécnica de Madrid; Coordination of European Transnational Research in Organic Food and Farming Systems; Ministerio de Economía y Competitividad; China Scholarship Council; Eusko Jaurlaritza; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Ministeriet for Fø devarer, Landbrug og Fiskeri; Global Environment Facility; Indian Council of Agricultural Research; Ministerio de Ciencia e Innovación; European Commission; National Natural Science Foundation of China; National Science Foundation; Department of Biotechnology, Ministry of Science and Technology, India; Emberi Eroforrások Minisztériuma; Department for Environment, Food and Rural Affairs, UK Government; Université Mohammed VI Polytechnique; Grønt Udviklings- og Demonstrations Program; Bundesamt für Landwirtschaft; Ministerio de Ciencia, Innovación y Universidades; Joint Research Centre; Center for Fertilization and Plant Nutrition; Fonds Wetenschappelijk Onderzoek; Comunidad de Madrid; Centre for Ecology and Hydrology; Xunta de Galicia; Interreg; Internationalt Center for Forskning i Økologisk Jordbrug og Fødevaresystemer; Australian Government; Banco Santander; University of Melbourne; Deutsche Bundesstiftung Umwelt; Vlaamse regering; Comunidad Autónoma de la Región de Murcia; Miljø- og Fødevareministeriet; Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria; Universidad de la República Uruguay; Ministry of Environment; Centro para el Desarrollo Tecnológico Industrial","keywords":"Random forest; Leaching (pedology); Environmental science; Nitrogen; Agriculture; Soil science; Remote sensing; Geography; Computer science; Machine learning; Soil water; Chemistry; Archaeology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005464595,0.0008855572,0.0006029744,0.001014993,0.0001934742,0.0003510934,0.000683266,0.000440499,0.0005546742],"category_scores_gemma":[0.0005890608,0.0002847432,0.0008160057,0.0009761494,0.0001316076,0.0004917589,0.0002598899,0.0003356338,0.0003055242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002697673,"about_ca_system_score_gemma":0.0003183517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009205364,"about_ca_topic_score_gemma":0.01100824,"domain_scores_codex":[0.9997249,0.0000542001,0.00001248333,0.0001138104,0.00005385072,0.00004067472],"domain_scores_gemma":[0.999809,0.00007069424,0.00003026202,0.00002242081,0.00005930007,0.000008297924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003964001,0.0004663587,0.02179021,0.0001987506,0.0004215957,0.0002193766,0.00007687712,0.6121871,0.08458927,0.0005438213,0.001395243,0.277715],"study_design_scores_gemma":[0.00001059258,0.00003454948,0.00447529,0.00000374668,0.00002198343,0.00002166792,0.00001045207,0.9918243,0.003177371,0.0002133479,0.0001947344,0.00001196831],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5230182,0.0007879317,0.4706379,0.000130978,0.00007197124,0.00008403994,0.0009213427,0.002321667,0.002025964],"genre_scores_gemma":[0.8505338,0.0001837368,0.1470506,0.00004594003,0.00002791926,0.0000695792,0.001083242,0.00005899368,0.0009463189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009205364,"threshold_uncertainty_score":0.01830357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01085999766933234,"score_gpt":0.1998103909394424,"score_spread":0.18895039327011,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}