{"id":"W4410813939","doi":"10.1175/jcli-d-24-0484.1","title":"Enhanced Global Tropical Cyclone Identification in ERA5 through Bayesian Inference and Dynamic Tracking (BIDTrack) Algorithm","year":2025,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Tropical and Extratropical Cyclones Research","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Tropical cyclone; Climatology; Bayesian probability; Identification (biology); Dynamic Bayesian network; Bayesian inference; Tracking (education); Inference; Environmental science; Algorithm; Meteorology; Computer science; Artificial intelligence; Geology; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001754503,0.0001127064,0.000277648,0.0000863721,0.00008640921,0.000144747,0.0002085595,0.00009208922,0.0001640583],"category_scores_gemma":[0.0001547165,0.0000851363,0.00006772679,0.0003689616,0.0001080962,0.0004647642,0.0000197987,0.0003485965,0.00001894378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000236691,"about_ca_system_score_gemma":0.00006257524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002323615,"about_ca_topic_score_gemma":0.002570677,"domain_scores_codex":[0.9984639,0.0001149483,0.0005986032,0.0001784811,0.000306828,0.0003372025],"domain_scores_gemma":[0.9993885,0.0001718937,0.000145231,0.00009660407,0.00008927705,0.0001084511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001444671,0.00004880421,0.5730258,0.00005042047,0.00001561472,0.00005234212,0.00005604046,0.0002132632,0.0004186634,0.0006259573,0.000008827143,0.4253398],"study_design_scores_gemma":[0.0005480725,0.0001305957,0.9775327,0.00008721249,0.00001244205,0.00002593657,0.0000728948,0.008628075,0.0001425519,0.01256652,0.0001704941,0.00008245205],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336339,0.0009881208,0.06277405,0.001126035,0.0003583433,0.00008774492,0.00002774355,0.000009639632,0.0009944591],"genre_scores_gemma":[0.9937497,0.002435009,0.00364384,0.00009032289,0.00005384259,3.995607e-7,0.000006617468,0.000001656709,0.00001854876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4252574,"threshold_uncertainty_score":0.3471758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196151448165803,"score_gpt":0.3108014742287421,"score_spread":0.298839959747084,"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."}}