{"id":"W2785118221","doi":"","title":"Using Tornado, Lightning, and Population Data to Identify Tornado Prone Areas in Canada","year":2012,"lang":"en","type":"article","venue":"26th Conference on Severe Local Storms (5 - 8 November 2012)","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Tornado; Lightning (connector); Meteorology; Population; Geography; Medicine; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005586956,0.0003263764,0.000336386,0.00226943,0.001870962,0.001768524,0.0009116069,0.0003189715,0.001345673],"category_scores_gemma":[0.002555692,0.0003118969,0.0004781926,0.004604097,0.000429752,0.0004889796,0.000744125,0.0005420602,0.0002180975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02701312,"about_ca_system_score_gemma":0.04317758,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9986993,"about_ca_topic_score_gemma":0.9993225,"domain_scores_codex":[0.9996171,0.00002691223,0.00002553696,0.00005385929,0.0001297929,0.0001468519],"domain_scores_gemma":[0.9978933,0.0001679263,0.0001268313,0.00003956868,0.00139421,0.0003782148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001682754,0.0001039625,0.9717078,0.00006924549,0.0001421671,0.000265099,0.00105367,0.006656172,0.0005238719,0.0004828201,0.004241439,0.01458532],"study_design_scores_gemma":[0.00003409105,0.00002464765,0.9728137,0.0000678649,0.00007521523,0.00005327407,0.005843205,0.01661093,0.0003854604,0.0001228626,0.003929173,0.00003960801],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989197,0.0002418701,0.0002577094,0.00033165,0.00001399102,0.00006302317,0.007135566,0.00003631161,0.002722762],"genre_scores_gemma":[0.9925618,0.0003662183,0.0007738903,0.00004219718,0.000003679744,0.00002173778,0.004335905,0.00001146854,0.00188328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02701312,"threshold_uncertainty_score":0.1959949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165786907856661,"score_gpt":0.3028569058667115,"score_spread":0.1862782150810454,"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."}}