{"id":"W4360610500","doi":"10.1080/02786826.2023.2193237","title":"Factors influencing ambient particulate matter in Delhi, India: Insights from machine learning","year":2023,"lang":"en","type":"article","venue":"Aerosol Science and Technology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Welch Foundation; National Science Foundation","keywords":"Particulates; Aerosol; Environmental science; Atmospheric sciences; Precipitation; Meteorology; Air pollution; Wind speed; Megacity; Relative humidity; New delhi; Climatology; Geography; Geology; Chemistry","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":[0.0005709067,0.0004356405,0.0002667562,0.0008231237,0.0002864799,0.0009711301,0.0006028569,0.0004689092,0.0006381807],"category_scores_gemma":[0.001452434,0.000211287,0.0005853949,0.000964085,0.0005557214,0.0004923996,0.0005144846,0.0006461183,0.0002713719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228185,"about_ca_system_score_gemma":0.000922012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1110568,"about_ca_topic_score_gemma":0.08274888,"domain_scores_codex":[0.9996867,0.0001070337,0.00002136941,0.00005971918,0.0000525834,0.00007270135],"domain_scores_gemma":[0.9990094,0.0005940675,0.0001646083,0.00006878811,0.0001127352,0.00005040456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001043675,0.0002337123,0.7721683,0.0001323745,0.0002056933,0.0007704135,0.0004324306,0.1943504,0.001824996,0.003547627,0.002047376,0.02418223],"study_design_scores_gemma":[0.00001281606,0.00006713279,0.7083487,0.00003136011,0.00008844458,0.0001839229,0.0006152778,0.2834522,0.0009482119,0.003985909,0.00221664,0.00004941817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895341,0.0004271225,0.003100559,0.001280299,0.00001861454,0.00002890769,0.001172845,0.00008757559,0.004349998],"genre_scores_gemma":[0.9981247,0.0002624431,0.0007366854,0.00005051273,0.00001369382,0.00000785882,0.0005290169,0.000007156001,0.0002681336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1110568,"threshold_uncertainty_score":0.2208208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02585725171718356,"score_gpt":0.2732213046203345,"score_spread":0.2473640529031509,"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."}}