{"id":"W2521652289","doi":"10.1175/mwr-d-16-0138.1","title":"GEPS-Based Monthly Prediction at the Canadian Meteorological Centre","year":2016,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Climate variability and models","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Environment and Climate Change Canada","keywords":"Climatology; Hindcast; Geopotential height; Predictability; Environmental science; Forecast skill; Northern Hemisphere; Meteorology; Initialization; Computer science; Geography; Precipitation; Statistics; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009051083,0.0007209036,0.0004233961,0.001216001,0.00110463,0.001310644,0.001243054,0.0003651809,0.007699436],"category_scores_gemma":[0.002585959,0.0002888068,0.0004681986,0.001877332,0.0002223534,0.0005602273,0.0005547762,0.0009928826,0.002139299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009790262,"about_ca_system_score_gemma":0.02341839,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9749277,"about_ca_topic_score_gemma":0.975665,"domain_scores_codex":[0.9990403,0.00005373738,0.00002875156,0.000151043,0.0006005461,0.0001255588],"domain_scores_gemma":[0.997467,0.00006190005,0.00006495538,0.0001270284,0.002106485,0.0001727233],"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":[0.0004467438,0.0001380341,0.1386875,0.0002455665,0.0002842379,0.0002204242,0.0002808585,0.3118005,0.004841283,0.007084133,0.4033276,0.1326432],"study_design_scores_gemma":[0.0001070423,0.00003279263,0.1388818,0.00009566241,0.00006245731,0.00002522486,0.0001512467,0.7221909,0.004114673,0.001354559,0.1328419,0.0001417349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3376089,0.002008621,0.05497069,0.003904453,0.001846836,0.0007044367,0.4500959,0.01154657,0.1373135],"genre_scores_gemma":[0.733012,0.0008924765,0.03674908,0.0002967172,0.0001633137,0.0001848891,0.2022133,0.0007060915,0.02578212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02507234,"threshold_uncertainty_score":0.0710336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02293608974552986,"score_gpt":0.2284322353193766,"score_spread":0.2054961455738468,"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."}}