{"id":"W2991100369","doi":"10.5194/gmd-13-2925-2020","title":"A multiphase CMAQ version 5.0 adjoint","year":2020,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; HORIZON EUROPE Excellent Science; Compute Canada; H2020 European Research Council; Health Canada; National Aeronautics and Space Administration; Health Effects Institute; ConocoPhillips; U.S. Environmental Protection Agency; National Science Foundation","keywords":"Advection; CMAQ; Data assimilation; Adjoint equation; Computer science; Meteorology; Applied mathematics; Air quality index; Mathematics; Physics; Thermodynamics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001602045,0.000841149,0.0005375058,0.0005027822,0.0006420742,0.001260033,0.00178447,0.001480441,0.009787766],"category_scores_gemma":[0.002299302,0.0006984021,0.001094314,0.0004797593,0.0004485918,0.001121121,0.001207228,0.001805569,0.002690451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007000847,"about_ca_system_score_gemma":0.001582197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005382176,"about_ca_topic_score_gemma":0.003285888,"domain_scores_codex":[0.9994853,0.0001050626,0.00003586836,0.00008530382,0.0002318378,0.00005666512],"domain_scores_gemma":[0.9990659,0.0001902276,0.0000651478,0.0001517878,0.000460687,0.00006629582],"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":[0.0003004098,0.0003493746,0.003986338,0.0003345883,0.0001431565,0.0002631003,0.0002050222,0.8214974,0.02615992,0.02736124,0.03389129,0.08550811],"study_design_scores_gemma":[0.00010947,0.00004838909,0.0003534111,0.00002221404,0.00001423455,0.00003289669,0.0000172522,0.9617249,0.007688524,0.00359987,0.02634953,0.00003921285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03716207,0.0002584996,0.9080136,0.0004416023,0.0006714598,0.0004403633,0.005902502,0.02783646,0.01927339],"genre_scores_gemma":[0.3396326,0.00014931,0.636237,0.0005160761,0.00008648572,0.0007034134,0.00924396,0.004322429,0.00910865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009787766,"threshold_uncertainty_score":0.03274333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03030705802477723,"score_gpt":0.1973291431059378,"score_spread":0.1670220850811606,"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."}}