{"id":"W2967424285","doi":"10.5194/gmd-12-5097-2019","title":"Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)","year":2019,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Climate variability and models","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Atmosphere (unit); Data assimilation; Climatology; Environmental science; Sea surface temperature; Atmospheric model; Sea ice; Initialization; Radiance; Satellite; Atmospheric sciences; Meteorology; Geology; Geography; Remote sensing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002533277,0.0001659388,0.0001397578,0.00001993639,0.0004321455,0.0001916112,0.0009247334,0.0001083273,0.0003812769],"category_scores_gemma":[0.00002240515,0.0001329931,0.00002357655,0.0004991315,0.00008149115,0.0004237489,0.0003428539,0.0001262099,0.0007475719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002172225,"about_ca_system_score_gemma":0.0003568776,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05158534,"about_ca_topic_score_gemma":0.3999811,"domain_scores_codex":[0.997466,0.00007655203,0.0004143045,0.0007926618,0.0007547629,0.0004956871],"domain_scores_gemma":[0.9986672,0.00002191503,0.00008208273,0.001060246,0.00001797305,0.0001505708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001807745,0.0001305615,0.1932915,0.0000588887,0.00001111161,0.000005548617,0.003017006,0.78994,0.00013785,0.001301171,0.01007917,0.002009111],"study_design_scores_gemma":[0.000182772,0.000005297779,0.1209201,0.00002449568,0.000006446193,0.000004740937,0.0002675162,0.8707466,0.000003124914,0.0001648569,0.007537363,0.0001367068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9722304,0.000007768127,0.007362177,0.0002893163,0.0008075873,0.0008911199,0.0002975023,0.00005358541,0.01806054],"genre_scores_gemma":[0.994487,0.000001518394,0.003798284,0.0001557541,0.00001064307,0.00002041919,0.000699751,0.000007961523,0.0008186548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3483957,"threshold_uncertainty_score":0.9608773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123348585488842,"score_gpt":0.2260730034510117,"score_spread":0.1948395175961233,"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."}}