{"id":"W3155261203","doi":"10.1029/2020sw002684","title":"Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)","year":2021,"lang":"en","type":"article","venue":"Space Weather","topic":"Ionosphere and magnetosphere dynamics","field":"Physics and Astronomy","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Science and Technology Facilities Council; University of Colorado Boulder; Università degli Studi dell'Aquila; Sveriges Geologiska Undersökning; Florida Institute of Technology; Alberta Agricultural Research Institute; National Aeronautics and Space Administration","keywords":"Space weather; Mesoscale meteorology; Satellite; Computer science; Precipitation; Solar wind; Meteorology; Electron precipitation; Environmental science; Magnetosphere; Physics","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":[0.002588036,0.0003515484,0.0004783283,0.0003110891,0.0003259594,0.001535753,0.00101116,0.0007984142,0.001250012],"category_scores_gemma":[0.003060651,0.0002026008,0.0004530082,0.0004447385,0.0005798825,0.001737194,0.0008643862,0.001395078,0.0002644949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007148059,"about_ca_system_score_gemma":0.001013003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01449607,"about_ca_topic_score_gemma":0.01085801,"domain_scores_codex":[0.9996991,0.0001336212,0.00001610464,0.00006377768,0.00006304192,0.00002442335],"domain_scores_gemma":[0.9989551,0.000555394,0.00007883032,0.0001363852,0.0002141212,0.00006010366],"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":[0.00003338925,0.00005918123,0.003003601,0.00002605968,0.00003763158,0.00003956312,0.0000310754,0.9558799,0.0004518761,0.01713818,0.001919689,0.02137988],"study_design_scores_gemma":[0.00000300247,0.000007120757,0.00009891601,0.000003099085,0.00000174626,0.000002425995,0.000004749101,0.9953994,0.0001097458,0.003823673,0.0005442073,0.000001878952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1286057,0.001375623,0.8522403,0.008744746,0.0001717171,0.00008865901,0.0006551083,0.001149043,0.006969181],"genre_scores_gemma":[0.7113904,0.0007029729,0.283698,0.0003896636,0.000179287,0.000107892,0.0006284323,0.0000909006,0.002812578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01449607,"threshold_uncertainty_score":0.02882338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03535058206168875,"score_gpt":0.2828647335329242,"score_spread":0.2475141514712354,"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."}}