{"id":"W3024417993","doi":"10.1175/waf-d-19-0259.1","title":"The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2)","year":2020,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Climate variability and models","field":"Environmental Science","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Environment and Climate Change Canada","funders":"","keywords":"Climatology; Teleconnection; Madden–Julian oscillation; Geopotential height; Environmental science; Forecast skill; Initialization; Northern Hemisphere; Sea surface temperature; Atmosphere (unit); Precipitation; Climate model; Hindcast; Geopotential; Atmospheric model; Data assimilation; Meteorology; Numerical weather prediction; El Niño Southern Oscillation; Climate change; Geology; Geography; Oceanography; Convection; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001286726,0.001278471,0.0005772837,0.001478995,0.001182213,0.001756182,0.002054831,0.0005174611,0.009439638],"category_scores_gemma":[0.002500352,0.0004026457,0.0006897157,0.003184423,0.0002286832,0.0007086222,0.000871171,0.0008157095,0.002708701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006779284,"about_ca_system_score_gemma":0.02450675,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9565511,"about_ca_topic_score_gemma":0.9408367,"domain_scores_codex":[0.9991841,0.00006982656,0.00003352086,0.0001597776,0.0004236582,0.000129204],"domain_scores_gemma":[0.9978265,0.00007276252,0.0000865301,0.0001426705,0.001649144,0.0002223794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000786268,0.0001458372,0.06240798,0.0003230621,0.0005448179,0.000141945,0.0001762612,0.1150844,0.003473973,0.006688104,0.6272141,0.1830133],"study_design_scores_gemma":[0.0004800286,0.00006597905,0.0683524,0.0001383636,0.0001932298,0.00004786127,0.000136792,0.6669775,0.004068609,0.002213497,0.257067,0.0002588091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1180487,0.00211474,0.07748054,0.002085052,0.00135236,0.0009869962,0.6789789,0.02978299,0.08916978],"genre_scores_gemma":[0.3878637,0.001384755,0.08188522,0.000536124,0.000156301,0.0007119831,0.5030878,0.001945482,0.02242858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04344887,"threshold_uncertainty_score":0.08740944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02441290730574258,"score_gpt":0.1975652266315532,"score_spread":0.1731523193258106,"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."}}