{"id":"W4392601132","doi":"10.5194/egusphere-egu24-7141","title":"Deep learning-derived anthropogenic and meteorological drivers of surface ozone change in China","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"China; Environmental science; Ozone; Climatology; Meteorology; Climate change; Atmospheric sciences; Geography; Geology; Oceanography; Archaeology","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":[0.0005836745,0.000669205,0.0003066372,0.0004956505,0.0002829511,0.0005120455,0.0006037934,0.0003811282,0.0006283359],"category_scores_gemma":[0.0007789168,0.0002042228,0.0006753342,0.0006499682,0.0002782372,0.0005657646,0.0006469406,0.0003931732,0.00007832353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146702,"about_ca_system_score_gemma":0.001880748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1344905,"about_ca_topic_score_gemma":0.08615723,"domain_scores_codex":[0.9998688,0.00002379017,0.000008576091,0.00003815854,0.00002338444,0.00003728846],"domain_scores_gemma":[0.9998109,0.0000428633,0.00002827561,0.00002061118,0.00006333317,0.00003408094],"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":[0.00009180556,0.00009034916,0.1956482,0.0001057995,0.00023616,0.0001944959,0.00007393264,0.7745048,0.002691592,0.001272607,0.001480273,0.02360998],"study_design_scores_gemma":[0.00001768865,0.0000126231,0.04124933,0.000008556111,0.00005791837,0.000007142588,0.00003454478,0.9567513,0.0007635403,0.0005231359,0.0005620206,0.00001222923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925385,0.0003567845,0.004304777,0.0004390257,0.00003564347,0.000007871007,0.0009337703,0.0001388318,0.001244876],"genre_scores_gemma":[0.9981658,0.0001203753,0.0005631013,0.00002938219,0.000009347875,0.000004993593,0.0007636954,0.000008663397,0.0003346982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1344905,"threshold_uncertainty_score":0.2674153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03590880343052954,"score_gpt":0.285783657520309,"score_spread":0.2498748540897795,"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."}}