{"id":"W2994453307","doi":"10.1029/2019ja027684","title":"A Gray‐Box Model for a Probabilistic Estimate of Regional Ground Magnetic Perturbations: Enhancing the NOAA Operational Geospace Model With Machine Learning","year":2020,"lang":"en","type":"article","venue":"Journal of Geophysical Research Space Physics","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Nuclear Safety and Security Commission; Rhode Island Space Grant Consortium; U.S. Geological Survey; Heliophysics Division; University of Michigan; National Aeronautics and Space Administration","keywords":"Probabilistic logic; Probabilistic forecasting; Decision tree; Space weather; Ensemble forecasting; Weather forecasting; Forecast skill; Statistical model","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.002139415,0.0004680705,0.000791188,0.0005818681,0.0003242386,0.0007524109,0.001460184,0.0008917034,0.00186989],"category_scores_gemma":[0.005377764,0.0004035431,0.0004765225,0.000435051,0.0007026985,0.001026206,0.0006776645,0.001096062,0.0003270071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008897557,"about_ca_system_score_gemma":0.001073764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01554483,"about_ca_topic_score_gemma":0.009311186,"domain_scores_codex":[0.9996393,0.000138547,0.00001497319,0.00008821161,0.00008040296,0.00003854077],"domain_scores_gemma":[0.9971192,0.002106132,0.0002474397,0.0001289499,0.0003324615,0.00006580217],"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":[0.00001724297,0.000008449172,0.0004168551,0.000003410078,0.000006726528,0.000007864176,0.000005183765,0.9941826,0.0001652827,0.001767559,0.0001133384,0.003305479],"study_design_scores_gemma":[9.235932e-7,0.000001375937,0.00002751578,5.818312e-7,5.056676e-7,6.335674e-7,2.452175e-7,0.9994726,0.00004116176,0.0004370688,0.00001687054,6.233499e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09814685,0.0001232615,0.89906,0.0005426189,0.00003531981,0.00003926301,0.0001837927,0.0005680851,0.001300753],"genre_scores_gemma":[0.9083723,0.00007900839,0.0891047,0.0001543853,0.00004969168,0.0001094922,0.0001944393,0.00005702267,0.001878992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01554483,"threshold_uncertainty_score":0.0309087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03861435142644511,"score_gpt":0.3084381141294018,"score_spread":0.2698237627029567,"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."}}