{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00136649,0.0007228471,0.0004041546,0.0008746518,0.000277779,0.0006043389,0.0007660869,0.0007759313,0.0004208858],"category_scores_gemma":[0.002730224,0.0003198594,0.000913056,0.0008583709,0.0003533134,0.0007796736,0.0006094905,0.0004133683,0.0001617144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008649164,"about_ca_system_score_gemma":0.000572027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04833829,"about_ca_topic_score_gemma":0.03396817,"domain_scores_codex":[0.9997184,0.00008021004,0.00001791353,0.0001121745,0.00002129104,0.00005006584],"domain_scores_gemma":[0.9992418,0.0002786713,0.0001298966,0.0001128832,0.0001878269,0.00004888956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001151308,0.00009060897,0.1029455,0.00002772392,0.0001609707,0.0001131815,0.00004180291,0.8777042,0.001264538,0.0003870714,0.000936921,0.01621238],"study_design_scores_gemma":[0.00001700866,0.00001809112,0.02484909,0.000007671829,0.0000188522,0.00001866159,0.00002756891,0.9738284,0.0004310238,0.0004116205,0.0003634961,0.000008507576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904243,0.0002442349,0.007529229,0.0001716094,0.00001881226,0.00001094807,0.001011957,0.0001839802,0.0004050122],"genre_scores_gemma":[0.9929894,0.00008993661,0.003986189,0.00003807194,0.00001432735,0.00001195987,0.002590972,0.00001715554,0.0002619798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04833829,"threshold_uncertainty_score":0.09611386,"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."}}