{"id":"W4206477494","doi":"10.1002/essoar.10510131.1","title":"Data-driven Atmospheric Drag and Radiation Pressure Models Based on GRACE-C Accelerometer Measurements for the Study of the Upper Atmosphere","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Drag; Accelerometer; Radiation pressure; Satellite; Physics; Drag coefficient; Atmospheric pressure; Meteorology; Geodesy; Remote sensing; Environmental science; Geology; Mechanics; Optics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003239574,0.0007504123,0.000511224,0.0003685324,0.0003113811,0.0007743318,0.001127501,0.0008053285,0.001069624],"category_scores_gemma":[0.0007444815,0.0003700631,0.000867945,0.0006263273,0.0003422268,0.0008043112,0.0005707064,0.001058038,0.0004303595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005040519,"about_ca_system_score_gemma":0.0008420752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02557992,"about_ca_topic_score_gemma":0.01474371,"domain_scores_codex":[0.999808,0.00003172331,0.00001361969,0.00006212947,0.000062258,0.00002234343],"domain_scores_gemma":[0.9998245,0.00005028544,0.00002939991,0.00002215939,0.00005835573,0.00001522062],"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":[0.0000267072,0.00004219678,0.002384144,0.00004436018,0.00003548401,0.00003931973,0.00003285192,0.9858968,0.002182234,0.001790733,0.0007011759,0.006824113],"study_design_scores_gemma":[0.000004825069,0.000009165178,0.001073097,0.000003794257,0.000006628052,0.000006600323,0.00000466067,0.9978628,0.0002090915,0.0002572691,0.0005535883,0.000008602551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4476482,0.001058511,0.5291653,0.00066213,0.000417541,0.0001583485,0.003789698,0.00301017,0.01409007],"genre_scores_gemma":[0.957837,0.0004409681,0.03277883,0.00007554299,0.00009898595,0.0001676933,0.002694447,0.0001773635,0.005729277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02557992,"threshold_uncertainty_score":0.05086207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1013592244914687,"score_gpt":0.2685626394647495,"score_spread":0.1672034149732808,"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."}}