{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001803074,0.0001560744,0.000136643,0.000005339062,0.0003262335,0.00008407842,0.0002631316,0.00005224862,0.003452879],"category_scores_gemma":[0.00003680313,0.0001359718,0.00004938796,0.0002466605,0.00006586029,0.0001436724,0.00004103576,0.000111147,0.001561766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009380073,"about_ca_system_score_gemma":0.0001948587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004839868,"about_ca_topic_score_gemma":0.00003119385,"domain_scores_codex":[0.9985043,0.00001381751,0.0002503149,0.0004994447,0.0003840041,0.0003480841],"domain_scores_gemma":[0.9993661,0.00001736214,0.00006233642,0.0001621102,0.00003748709,0.0003545865],"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":[0.0004133037,0.0002426738,0.0384958,0.0002994961,0.00008149366,0.0001784597,0.01275654,0.5632901,0.027132,0.0000372281,0.0977913,0.2592815],"study_design_scores_gemma":[0.0005183972,0.00002322106,0.005319464,0.00001919108,0.000007098376,0.000004409459,0.0002001208,0.8943628,0.01189712,0.00004848745,0.08724843,0.0003512481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9351139,0.0001007691,0.05665305,0.0007185151,0.0004261715,0.0002044158,0.0001301454,0.0001219094,0.006531098],"genre_scores_gemma":[0.9437876,0.0000055455,0.05155259,0.0007550225,0.00004650338,0.000002093743,0.0003938717,0.000003667748,0.003453142],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3310727,"threshold_uncertainty_score":0.9992157,"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."}}