{"id":"W4250515157","doi":"10.26434/chemrxiv.13724026","title":"Importance of Meteorology and Chemistry in Determining Air Pollutant Levels During COVID-19 Lockdown in Indian Cities","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"COVID-19 impact on air quality","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Ozone; Coronavirus disease 2019 (COVID-19); Environmental science; Air pollution; Meteorology; Atmospheric chemistry; Normalization (sociology); Air pollutants; Pollutant; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Atmospheric sciences; 2019-20 coronavirus outbreak; Geography; Chemistry; Physics; Infectious disease (medical specialty)","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":[0.0002516175,0.0002761885,0.0002636751,0.0006662107,0.0005778042,0.001357113,0.0003892584,0.0003942382,0.00110766],"category_scores_gemma":[0.00066214,0.000195129,0.0002445651,0.0008552,0.0003580574,0.0004365308,0.0007173489,0.0005701714,0.0002975046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194302,"about_ca_system_score_gemma":0.000689603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1141897,"about_ca_topic_score_gemma":0.1238873,"domain_scores_codex":[0.9997632,0.00003146683,0.00001824284,0.00006763354,0.00004690588,0.00007257923],"domain_scores_gemma":[0.9995518,0.00006526301,0.00009539817,0.00004968406,0.0001396674,0.0000981252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003317705,0.0001001972,0.9761453,0.00007650504,0.00006853428,0.000301054,0.0004962479,0.002177655,0.008844906,0.0001584302,0.001430895,0.009868391],"study_design_scores_gemma":[0.000002988289,0.00002422515,0.9960372,0.000006804178,0.00002034191,0.00003598617,0.0006163503,0.0009899555,0.001285806,0.00002557088,0.0009432146,0.00001144634],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958371,0.0001384314,0.0002196822,0.0001620464,0.00002042291,0.00001721368,0.00156596,0.00006116818,0.001978036],"genre_scores_gemma":[0.9985157,0.00006059612,0.0001767566,0.00003146142,0.000008523622,0.000009105292,0.000834503,0.00001053369,0.000352773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1141897,"threshold_uncertainty_score":0.2270502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03706909356711635,"score_gpt":0.3061176299493786,"score_spread":0.2690485363822623,"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."}}