{"id":"W2132889984","doi":"10.1175/bams-d-11-00154.1","title":"IMILAST: A Community Effort to Intercompare Extratropical Cyclone Detection and Tracking Algorithms","year":2012,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Climate variability and models","field":"Environmental Science","cited_by":686,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Fundação para a Ciência e a Tecnologia; Swiss Re; Deutsche Forschungsgemeinschaft; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Extratropical cyclone; Cyclone (programming language); Environmental science; Meteorology; Climatology; Storm; Middle latitudes; Consistency (knowledge bases); Computer science; Geography; 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":[],"consensus_categories":[],"category_scores_codex":[0.0009265765,0.000134132,0.0002753465,0.00000523243,0.0002856582,0.00001295371,0.0003135363,0.00005909606,0.0005118002],"category_scores_gemma":[0.0001558376,0.00008668545,0.0001927864,0.0001680077,0.0009472877,0.00002984817,0.0007626862,0.0003822021,0.00003163033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000086897,"about_ca_system_score_gemma":0.000001762665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001572056,"about_ca_topic_score_gemma":0.00003650033,"domain_scores_codex":[0.9987055,0.0003563982,0.0002223313,0.0001816102,0.0002036269,0.0003305192],"domain_scores_gemma":[0.999132,0.0002735396,0.000125657,0.0003032064,0.000008431834,0.0001571249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003896471,0.001628612,0.699179,0.00005111049,0.0001159518,6.097043e-7,0.007227975,0.001702717,0.1931645,0.0001132967,0.004141522,0.09228508],"study_design_scores_gemma":[0.0001716228,0.0003614809,0.9843355,0.000007468557,0.00003575853,0.00001241016,0.0009898959,0.0006741806,0.002048676,0.0002231228,0.01098377,0.0001561058],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943084,0.00001626139,0.003226044,0.001766735,0.00005771786,0.0002084012,0.000006302136,0.00003283266,0.0003773817],"genre_scores_gemma":[0.9855958,0.00001680714,0.01216359,0.002141881,0.0000356507,0.00002120329,6.28076e-7,0.000007267699,0.00001711994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2851565,"threshold_uncertainty_score":0.5603853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02780669047918569,"score_gpt":0.2603885820232089,"score_spread":0.2325818915440232,"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."}}