{"id":"W4415351090","doi":"10.1021/acs.estlett.5c00771","title":"Machine-Learning-Driven Reconstruction of Organic Aerosol Sources across Dense Monitoring Networks in Europe","year":2025,"lang":"en","type":"article","venue":"Environmental Science & Technology Letters","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"HORIZON EUROPE Marie Sklodowska-Curie Actions; H2020 European Institute of Innovation and Technology; Paul Scherrer Institut; National Key Research and Development Program of China; Région Hauts-de-France; Fundação para a Ciência e a Tecnologia; Région Occitanie Pyrénées-Méditerranée; Bundesamt für Umwelt; Région Normandie; Akademie Věd České Republiky; Ministry of Environment; Agence de l'Environnement et de la Maîtrise de l'Energie; Ministerul Cercetării, Inovării şi Digitalizării; Consell Català de Recerca i Innovació; National Natural Science Foundation of China; Academia Româna; Agence Nationale de la Recherche; Environmental Protection Agency; San Diego Supercomputer Center; Swiss Data Science Center; European Regional Development Fund; European Commission; Department of the Environment, Climate and Communications; Javna Agencija za Raziskovalno Dejavnost RS; Hungarian Research Network","keywords":"Aerosol; Apportionment; Particulates; Biomass burning; Total organic carbon; Pollution; Data set","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005435772,0.0001877001,0.0002033426,0.0002306118,0.0004804384,0.00003013895,0.0006533055,0.0001303064,0.00006938579],"category_scores_gemma":[0.0001147566,0.0001927581,0.00003564015,0.002239622,0.003230999,0.0002942358,0.0008067818,0.0005476689,0.00004334707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004817205,"about_ca_system_score_gemma":0.00001146038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000912351,"about_ca_topic_score_gemma":0.00002113512,"domain_scores_codex":[0.9980887,0.00006201664,0.0003631828,0.0005788495,0.0003085493,0.0005986766],"domain_scores_gemma":[0.9994051,0.00004459765,0.0001822244,0.0003075505,0.000003138756,0.00005733852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005619784,0.00002471292,0.6307243,0.000001865006,0.000002786675,0.00000484255,0.0002064023,0.01383152,0.3413098,0.000004151022,0.000002619269,0.01388145],"study_design_scores_gemma":[0.0007873935,0.0001536416,0.6450431,0.0001907524,0.00001962207,0.00006887445,0.002756196,0.01387912,0.3359444,0.00005161765,0.0005496133,0.0005556945],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997411,0.00008312074,0.001147081,0.0005760194,0.00036224,0.0001408735,0.000001367162,0.000104266,0.000173999],"genre_scores_gemma":[0.9985316,0.00006767411,0.001173824,0.00006011941,0.00002470716,0.00001010988,9.740543e-7,0.00001292191,0.0001181059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01431884,"threshold_uncertainty_score":0.9994816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006482156417203571,"score_gpt":0.2222775234883145,"score_spread":0.2157953670711109,"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."}}