{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001255064,0.0006846045,0.0002795554,0.0003994282,0.0003017523,0.0004831062,0.0008152385,0.0004995812,0.0008830517],"category_scores_gemma":[0.002896775,0.0002675198,0.0008485609,0.000886116,0.0001795663,0.0005135794,0.0003579045,0.0005782468,0.0001487803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050076,"about_ca_system_score_gemma":0.001160427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09961622,"about_ca_topic_score_gemma":0.05855574,"domain_scores_codex":[0.9996756,0.00009782144,0.00002285921,0.00008112118,0.00007390684,0.00004862068],"domain_scores_gemma":[0.9991917,0.0002007143,0.00008544909,0.00009790863,0.0003935815,0.00003070202],"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":[0.0001473203,0.00009311602,0.1130885,0.00006011007,0.0002676405,0.00006845193,0.00004483822,0.8667136,0.003673672,0.001194817,0.002049106,0.01259886],"study_design_scores_gemma":[0.00009241139,0.00005594851,0.05914285,0.00002023407,0.00009962149,0.00002512363,0.00005432824,0.9315762,0.005779526,0.0006230722,0.002495918,0.00003468817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621372,0.0002978233,0.0285,0.0003146655,0.000173686,0.00007646941,0.004435306,0.000659884,0.00340498],"genre_scores_gemma":[0.9902551,0.0000537758,0.007073969,0.0000574282,0.000008331179,0.00002875914,0.002131694,0.00004446969,0.0003464179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09961622,"threshold_uncertainty_score":0.1980728,"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."}}