{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001593551,0.001531973,0.00102838,0.002158496,0.0005709004,0.002309366,0.00262963,0.001506192,0.03872847],"category_scores_gemma":[0.006205302,0.0004888074,0.0009375794,0.004019946,0.000241948,0.00218881,0.001595815,0.00137352,0.02948648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008750895,"about_ca_system_score_gemma":0.002426846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02294513,"about_ca_topic_score_gemma":0.01098016,"domain_scores_codex":[0.9986812,0.0001859955,0.0002652832,0.0003446347,0.0004236943,0.00009917554],"domain_scores_gemma":[0.9965069,0.0007052829,0.0002972739,0.0009818103,0.001195299,0.000313546],"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":[0.0003356394,0.00005884089,0.006017686,0.0007456677,0.0001176877,0.0001471311,0.00006550486,0.01115015,0.001146145,0.003338761,0.9556707,0.02120602],"study_design_scores_gemma":[0.0005713319,0.00008336858,0.01475427,0.0002596516,0.00009221885,0.0001267481,0.0001957019,0.05524937,0.004157084,0.006997048,0.917366,0.000147113],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002402595,0.0001350093,0.003119356,0.0001944073,0.00008843639,0.00009226966,0.9839663,0.006375893,0.003625767],"genre_scores_gemma":[0.006125833,0.00008945599,0.002630829,0.00004887281,0.00001809039,0.0001358425,0.9899045,0.0004070841,0.0006395219],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03872847,"threshold_uncertainty_score":0.1295596,"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."}}