{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003858885,0.000123875,0.0001639976,0.0002601634,0.0004225358,0.00004080641,0.0002872545,0.0001353771,0.0001684544],"category_scores_gemma":[0.0002039508,0.00009833195,0.00001117184,0.002575506,0.0009792015,0.0004138997,0.0005083323,0.0003302696,0.0006289675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000152916,"about_ca_system_score_gemma":0.00003520787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002022308,"about_ca_topic_score_gemma":0.001076587,"domain_scores_codex":[0.9983961,0.00003278708,0.0002297691,0.0004400053,0.0003274028,0.0005739402],"domain_scores_gemma":[0.9995113,0.00005784041,0.00007367877,0.0001998955,0.00001002997,0.000147226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001985472,0.00001426512,0.9717992,0.000003092035,0.000001149339,0.00001157626,0.001953814,0.0001498087,0.02403134,0.0001028542,0.00009913716,0.001831788],"study_design_scores_gemma":[0.0002191619,0.00008393585,0.9660235,0.00002289077,0.000002946136,0.000002292527,0.001020571,0.003466984,0.02564031,0.002561053,0.000777938,0.0001783961],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961645,0.00003666881,0.00001016197,0.003158737,0.0000620775,0.00013229,0.000001919418,0.0001490356,0.0002845802],"genre_scores_gemma":[0.9987767,0.0000359205,0.00008368155,0.00103265,0.000005241156,0.0000123236,0.000003296376,0.000006512591,0.00004364193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005775671,"threshold_uncertainty_score":0.8084315,"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."}}