{"id":"W2471266319","doi":"10.1175/bams-d-16-0017.1","title":"The Subseasonal to Seasonal (S2S) Prediction Project Database","year":2016,"lang":"en","type":"article","venue":"Bulletin of the American Meteorological Society","topic":"Climate variability and models","field":"Environmental Science","cited_by":1111,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Environment Research Council; Russian Science Foundation; Sight Research UK; National Aeronautics and Space Administration; California Institute of Technology; Jet Propulsion Laboratory","keywords":"Predictability; Madden–Julian oscillation; Climatology; Teleconnection; Environmental science; Forecast skill; Meteorology; Hindcast; Range (aeronautics); Landfall; Database; Weather prediction; Tropical cyclone; Weather forecasting; Computer science; Precipitation; Geography; Statistics; Mathematics; Convection; Geology; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001286349,0.0001354468,0.0001814557,0.000003108792,0.0003382467,0.00001570838,0.0006750533,0.00004274906,0.001479813],"category_scores_gemma":[0.0006114371,0.00005360949,0.0002807979,0.0002464852,0.001696482,0.00002354591,0.0008350661,0.0001336328,0.0001805728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001234833,"about_ca_system_score_gemma":0.00001784758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003804899,"about_ca_topic_score_gemma":0.0000145542,"domain_scores_codex":[0.9982292,0.0003169563,0.0002247086,0.0003715455,0.0004931065,0.0003644853],"domain_scores_gemma":[0.9984377,0.0007809278,0.0001679644,0.0004957716,0.00001641918,0.0001012286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008995692,0.000557468,0.2161308,0.00001355828,0.0001283671,0.000001303331,0.0003818115,0.0005491566,0.1113343,0.001604564,0.6186132,0.04978593],"study_design_scores_gemma":[0.0004257641,0.0005124549,0.3237514,0.00001954474,0.00004854615,0.000006999841,0.0002145447,0.0006654069,0.001104449,0.0009232591,0.6721079,0.0002197207],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9658173,0.00001377609,0.001087948,0.03132354,0.00008164212,0.0003974597,0.0001705203,0.00004199444,0.001065822],"genre_scores_gemma":[0.9896678,0.00013851,0.005410247,0.003841143,0.00006503313,0.00008305083,0.000002529912,0.000009811726,0.0007818731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1102299,"threshold_uncertainty_score":0.999433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884894773701504,"score_gpt":0.2471512537782404,"score_spread":0.2283023060412253,"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."}}