{"id":"W4404711739","doi":"10.5194/gmd-17-8373-2024","title":"Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ","year":2024,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nuclear Safety and Security Commission; ConocoPhillips; National Aeronautics and Space Administration","keywords":"Environmental science; CMAQ; Range (aeronautics); Ozone; Atmospheric sciences; Climatology; Ozone layer; Meteorology; Geography; Physics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004837513,0.0001670359,0.0001939089,0.00004565522,0.0001594569,0.0001339322,0.0001322004,0.00007801611,0.0007780635],"category_scores_gemma":[0.000007707713,0.0001477374,0.00004013283,0.0005036325,0.0001698951,0.0001875901,0.00003010055,0.0001385172,0.00006864075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002293,"about_ca_system_score_gemma":0.0001829004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003947606,"about_ca_topic_score_gemma":0.0005928319,"domain_scores_codex":[0.9984117,0.00002495786,0.0003992326,0.0005349388,0.0003148128,0.0003144138],"domain_scores_gemma":[0.9995856,0.00005710719,0.00005588606,0.0001695487,0.00003020977,0.0001016933],"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":[0.0001034129,0.0001229747,0.05274003,0.0003179354,0.00004839365,0.00003140863,0.004156319,0.5843779,0.006391168,0.00004227596,0.002575727,0.3490925],"study_design_scores_gemma":[0.0001714265,0.00001448749,0.01946501,0.0001024226,0.000005673036,0.00001655759,0.000280362,0.9641374,0.00312642,0.0001242966,0.01233353,0.0002224613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9619483,0.001552003,0.03426499,0.0000249961,0.001055668,0.0001390853,0.00002894204,0.00004299508,0.0009429859],"genre_scores_gemma":[0.9875439,0.0001797207,0.005051031,0.00001304347,0.00002495836,0.000003210094,0.0001289327,0.000005624473,0.007049531],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3797594,"threshold_uncertainty_score":0.851925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04707664048024702,"score_gpt":0.2337438769336141,"score_spread":0.1866672364533671,"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."}}