{"id":"W3087471357","doi":"10.1029/2020ea001321","title":"NRLMSIS 2.0: A Whole‐Atmosphere Empirical Model of Temperature and Neutral Species Densities","year":2020,"lang":"en","type":"article","venue":"Earth and Space Science","topic":"Atmospheric Ozone and Climate","field":"Earth and Planetary Sciences","cited_by":452,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"Office of Naval Research; National Science Foundation; Bundesministerium für Digitalisierung und Wirtschaftsstandort; Canon Foundation for Scientific Research; National Aeronautics and Space Administration; Canadian Space Agency","keywords":"Thermosphere; Atmospheric sciences; Stratosphere; Mesosphere; Troposphere; Altitude (triangle); Atmosphere (unit); Atmospheric models; Earth's magnetic field; Environmental science; Atmospheric temperature; Physics; Ionosphere; Meteorology; Geophysics; Mathematics; Geometry","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":[0.0005058802,0.0008353155,0.0005827992,0.0003457723,0.0005188637,0.001087726,0.002869353,0.0009477932,0.00565263],"category_scores_gemma":[0.001243703,0.0007274754,0.0008055757,0.000654955,0.0004235517,0.001136406,0.0006734049,0.001196451,0.001548856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00186449,"about_ca_system_score_gemma":0.001811548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07218935,"about_ca_topic_score_gemma":0.0372871,"domain_scores_codex":[0.9998249,0.00003851625,0.000008560224,0.00004441504,0.00005830736,0.0000252464],"domain_scores_gemma":[0.9995888,0.00009706897,0.0000450935,0.00005290014,0.0001601855,0.00005602395],"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":[0.00007778488,0.00004765363,0.003689393,0.00002279333,0.00003195562,0.00003670102,0.00003143177,0.9854749,0.001242407,0.002997758,0.003849491,0.00249767],"study_design_scores_gemma":[0.00005909015,0.00001010922,0.0006280625,0.000002649565,0.00000508611,0.000004661619,0.000005533424,0.9967192,0.0003044317,0.000570673,0.001681635,0.000008842149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7514902,0.0003341251,0.1261885,0.001887932,0.0004026967,0.0002790474,0.03233249,0.01303281,0.07405235],"genre_scores_gemma":[0.9538811,0.000140052,0.02422666,0.0001628578,0.00008530855,0.0002704417,0.01049051,0.0009960805,0.00974698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07218935,"threshold_uncertainty_score":0.1435383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247947925407449,"score_gpt":0.217151795572302,"score_spread":0.1946723163182275,"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."}}