{"id":"W4408221795","doi":"10.1038/s41598-025-92788-x","title":"Explainable AI analysis for smog rating prediction","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Data science","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":[0.002236519,0.00008331947,0.0001318962,0.0001343481,0.0008321441,0.0002081767,0.00009007213,0.00004553959,0.0001567034],"category_scores_gemma":[0.0003318413,0.00007885725,0.0001268265,0.001400635,0.0001421622,0.0002321011,0.00009825367,0.00006496478,0.00001127136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001416161,"about_ca_system_score_gemma":0.00002393938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000130624,"about_ca_topic_score_gemma":0.00002859272,"domain_scores_codex":[0.9984457,0.00002769362,0.0003785525,0.0005867039,0.000297671,0.000263681],"domain_scores_gemma":[0.999258,0.00005007603,0.0001522813,0.0004537695,0.0000312388,0.00005460075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007966647,0.00006875591,0.8791318,0.00003466727,0.0001065209,0.00001913483,0.0007615741,0.04473858,0.01079492,0.0001118128,0.04949231,0.01473193],"study_design_scores_gemma":[0.0004608882,0.00009994712,0.145196,0.0001414185,0.0009408603,0.0000327486,0.001732031,0.2199262,0.08001707,0.0451424,0.5055939,0.0007165779],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.81856,0.00003593663,0.1602925,0.0003351359,0.007344822,0.0004189543,0.000004452541,0.000165485,0.01284269],"genre_scores_gemma":[0.9691916,2.490899e-7,0.002672924,0.00003478179,0.0000504102,0.00005601518,0.00003678061,0.000004631444,0.02795268],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7339358,"threshold_uncertainty_score":0.6400264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01784661605961044,"score_gpt":0.2762540738930707,"score_spread":0.2584074578334602,"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."}}