{"id":"W1981519336","doi":"10.1175/bams-d-12-00032.1","title":"The NCEP–FNMOC Combined Wave Ensemble Product: Expanding Benefits of Interagency Probabilistic Forecasts to the Oceanic Environment","year":2013,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Probabilistic logic; Upgrade; Meteorology; Product (mathematics); Environmental science; Probabilistic forecasting; Computer science; Ensemble forecasting; Climatology; Altimeter; Geography; Geology; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001051918,0.000205124,0.0003808683,0.00001528162,0.0005248985,0.00004120396,0.0009019663,0.0000440355,0.002726811],"category_scores_gemma":[0.000671819,0.00008174351,0.0003305949,0.0003842983,0.00098364,0.00002149886,0.000257908,0.0002477244,0.0001394995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001129259,"about_ca_system_score_gemma":0.00001291582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004739599,"about_ca_topic_score_gemma":0.0000191256,"domain_scores_codex":[0.9977643,0.000458822,0.0005058152,0.0003709993,0.0004479887,0.0004520582],"domain_scores_gemma":[0.9972144,0.001576812,0.000421307,0.0006025719,0.0000639145,0.0001209622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008602572,0.0006697547,0.09386813,0.00009325449,0.0006143699,0.000001475631,0.003253398,0.195073,0.004086378,0.003342957,0.1059388,0.5921983],"study_design_scores_gemma":[0.0003327272,0.002010554,0.9570683,0.00001872729,0.00008075009,0.000004409651,0.0008503995,0.007505525,0.0004173531,0.01230814,0.01910582,0.0002972529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841089,0.0005077209,0.0001679949,0.01252385,0.0001704216,0.001128582,0.0000262486,0.00001975616,0.00134658],"genre_scores_gemma":[0.9953387,0.0002076115,0.002366992,0.001614317,0.0000675812,0.00003133788,0.000006058651,0.000004958296,0.0003624562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8632002,"threshold_uncertainty_score":0.9981849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158819156442597,"score_gpt":0.2072967021467899,"score_spread":0.185708510582364,"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."}}