{"id":"W3196172941","doi":"10.1029/2021ms002570","title":"A Data Set for Intercomparing the Transient Behavior of Dynamical Model‐Based Subseasonal to Decadal Climate Predictions","year":2021,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Climate variability and models","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Hindcast; Climatology; Set (abstract data type); Forecast skill; Data set; Range (aeronautics); Climate model; Coupled model intercomparison project; Transient (computer programming); Computer science; Environmental science; Meteorology; Climate change; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006263371,0.0002916864,0.0002299641,0.001233709,0.0003193415,0.0004461378,0.0004240351,0.0004086901,0.0008676221],"category_scores_gemma":[0.001655626,0.0001238028,0.0002648154,0.001187577,0.0001513069,0.0003657751,0.0005266364,0.0004683256,0.0002698906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002776236,"about_ca_system_score_gemma":0.000411636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009107851,"about_ca_topic_score_gemma":0.00929134,"domain_scores_codex":[0.9997351,0.00005480457,0.00004261945,0.00007464269,0.00006641651,0.00002643746],"domain_scores_gemma":[0.9984103,0.0003913331,0.0002379133,0.0004965409,0.0003683336,0.00009546623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00133992,0.001565175,0.5087458,0.0003939516,0.0006592143,0.0006636102,0.000691595,0.2543292,0.03033127,0.006041713,0.04164143,0.1535971],"study_design_scores_gemma":[0.0001727424,0.0004229305,0.7128976,0.00006061215,0.0001046058,0.0002213513,0.0005301982,0.2233364,0.02087127,0.001830365,0.039453,0.0000989126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.8865692,0.00009382816,0.01012155,0.0001087435,0.00009056324,0.00009953505,0.0991172,0.0007624289,0.003036803],"genre_scores_gemma":[0.8675805,0.00005217472,0.008886272,0.00002178023,0.00002749103,0.0002136847,0.1226942,0.00006080682,0.0004631455],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.009107851,"threshold_uncertainty_score":0.01810968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07536255300938054,"score_gpt":0.3408097905734865,"score_spread":0.265447237564106,"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."}}