{"id":"W2122058652","doi":"10.1175/2010waf2222326.1","title":"TIGGE: Comparison of the Prediction of Northern Hemisphere Extratropical Cyclones by Different Ensemble Prediction Systems","year":2010,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Climate variability and models","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Extratropical cyclone; Predictability; Meteorology; Environmental science; Climatology; Northern Hemisphere; Cyclone (programming language); Forecast verification; Tropical cyclone forecast model; Forecast skill; Weather forecasting; Computer science; Geography; Statistics; Mathematics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001139231,0.00008342569,0.0001508322,0.000005430849,0.00009232325,0.00001011809,0.00008263806,0.00008009062,0.00007576732],"category_scores_gemma":[0.00002623087,0.00005403427,0.00004240122,0.00004487054,0.0001440668,0.00005831443,0.00006940779,0.0001333062,9.123058e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000169332,"about_ca_system_score_gemma":0.000002951409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004594268,"about_ca_topic_score_gemma":0.0004647814,"domain_scores_codex":[0.9992439,0.00003141379,0.0002819421,0.0001541644,0.0001758575,0.0001127075],"domain_scores_gemma":[0.9996088,0.00005618413,0.0001308254,0.0001576536,0.000009444126,0.00003706435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001035415,0.00007721548,0.7389048,0.00003503613,0.000005305861,2.525754e-8,0.0004261957,0.0008444677,0.2571035,0.00004222789,0.00004932609,0.002501512],"study_design_scores_gemma":[0.0007426676,0.0002248903,0.5190469,0.000136146,0.00008159375,0.0000188016,0.0008085252,0.4474181,0.03000584,0.0005515777,0.0007849964,0.0001799959],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959257,0.00004541693,0.001152184,0.00002044194,0.0002009145,0.0001709481,0.00005540872,0.00001468841,0.002414304],"genre_scores_gemma":[0.9997709,0.000004839053,0.00006358872,0.00000241335,0.00003409527,0.00000884161,0.000005292262,0.000007136827,0.0001028569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4465736,"threshold_uncertainty_score":0.2203453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159721735775627,"score_gpt":0.2188883162504712,"score_spread":0.1972910988927149,"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."}}