{"id":"W3092641105","doi":"10.5194/egusphere-egu2020-1166","title":"Multi-scale Analysis of Electromagnetic Energy Input using Swarm: Quantifying Key Scales in Magnetosphere-Ionosphere Coupling","year":2020,"lang":"en","type":"article","venue":"","topic":"Earthquake Detection and Analysis","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Alberta","funders":"","keywords":"Ionosphere; Physics; Magnetosphere; Geophysics; Context (archaeology); Poynting vector; Computational physics; Energy budget; Atmospheric sciences; Magnetic field; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001788323,0.000184598,0.0005085021,0.0002253707,0.00009910702,0.00005746122,0.0001952693,0.00009600884,0.005162679],"category_scores_gemma":[0.00003589576,0.0001680417,0.0002567869,0.00334048,0.00006529083,0.0001675002,0.00001500755,0.0001438877,0.00003080421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000590819,"about_ca_system_score_gemma":0.00003744461,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01591132,"about_ca_topic_score_gemma":0.2062033,"domain_scores_codex":[0.9983698,0.00007160985,0.0005309,0.000416419,0.0002779645,0.0003333163],"domain_scores_gemma":[0.9993452,0.00009411797,0.0001527493,0.0001684943,0.00005731273,0.0001821575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002272113,0.0000229824,0.5678018,0.00001354884,0.0001464245,0.000006601714,0.0001373941,0.420006,0.007329555,0.000007888582,0.000006906079,0.004498199],"study_design_scores_gemma":[0.000226832,0.00009057568,0.2073765,0.000007937454,0.000238116,0.000001000691,0.0004699696,0.7892798,0.002056081,0.000004771834,0.0000826462,0.0001658304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9812195,0.001001052,0.01686134,0.0001155798,0.00004654289,0.00005053594,0.00001325593,0.00005421097,0.0006379733],"genre_scores_gemma":[0.9851677,0.0001966301,0.01414143,0.0002810685,0.0000308548,4.320489e-7,0.00005244479,0.000005782165,0.0001236371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3692738,"threshold_uncertainty_score":0.9957467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03980757696543908,"score_gpt":0.2480529595296987,"score_spread":0.2082453825642596,"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."}}