{"id":"W2770241145","doi":"10.1002/asl.795","title":"Tornado identification and forewarning with very high frequency windprofiler radars","year":2017,"lang":"en","type":"article","venue":"Atmospheric Science Letters","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Western University; Dynamic Systems Analysis (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tornado; Supercell; Fujita scale; Tropopause; Radar; Environmental science; Meteorology; Depth sounding; Troposphere; Clear-air turbulence; Stratosphere; Remote sensing; Atmospheric sciences; Turbulence; Geology; Geography; Computer science; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001952702,0.0002698025,0.0001828738,0.0008863985,0.0001612823,0.0003305732,0.0002945864,0.0001715146,0.001488008],"category_scores_gemma":[0.0004108681,0.0001206504,0.000138595,0.0005584629,0.0001277875,0.0002423726,0.0002491144,0.0001879621,0.0001497856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002239799,"about_ca_system_score_gemma":0.0002864038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02382349,"about_ca_topic_score_gemma":0.05706758,"domain_scores_codex":[0.9999211,0.00001281834,0.000003021121,0.00001533908,0.00002034526,0.00002743425],"domain_scores_gemma":[0.9998363,0.00003504958,0.00003740125,0.0000185333,0.00004742511,0.00002522885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001074354,0.0002697317,0.5391905,0.0001476498,0.0001762943,0.0007949089,0.0008281102,0.1073004,0.1671997,0.001051803,0.002712761,0.1792538],"study_design_scores_gemma":[0.00004803197,0.0001906662,0.795201,0.00001750597,0.00003738244,0.0002037785,0.0003189819,0.1909027,0.01108129,0.0002500273,0.001706543,0.00004207601],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886336,0.00006885719,0.00805481,0.00002606018,0.000008745172,0.00003147414,0.000545311,0.0002663233,0.00236481],"genre_scores_gemma":[0.9937418,0.0000313395,0.005362844,0.000004713668,0.000005382839,0.000007879032,0.000417992,0.00001235224,0.0004157462],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02382349,"threshold_uncertainty_score":0.04736966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306261355987039,"score_gpt":0.2126620903233314,"score_spread":0.199599476763461,"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."}}