{"id":"W1968473082","doi":"10.1007/s00382-012-1600-0","title":"Real-time multi-model decadal climate predictions","year":2012,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":143,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Japan Agency for Marine-Earth Science and Technology; Bundesministerium für Bildung und Forschung; National Oceanic and Atmospheric Administration; Sight Research UK; Department for Environment, Food and Rural Affairs, UK Government; Agence Nationale de la Recherche; Natural Environment Research Council; Met Office","keywords":"Climatology; Initialization; Environmental science; Forecast skill; Climate model; Climate change; Econometrics; Geology; Computer science; Oceanography; Economics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007446714,0.0002613392,0.0002566562,0.00004540508,0.0004261303,0.00004532922,0.0002985821,0.0001956003,0.001172422],"category_scores_gemma":[0.00004199753,0.0002577725,0.0001298464,0.0002107652,0.0002512244,0.0006786464,0.0005895366,0.0002063419,0.002613164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005760464,"about_ca_system_score_gemma":0.00001266989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001331361,"about_ca_topic_score_gemma":0.00019743,"domain_scores_codex":[0.9977045,0.00005986378,0.0004088631,0.0004073049,0.0002861361,0.00113333],"domain_scores_gemma":[0.9988703,0.00007963812,0.0001186016,0.0005763699,0.00001362637,0.0003414683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001387192,0.002671656,0.5788756,0.0001634435,0.00005452962,0.000007374116,0.00273845,0.3681624,0.02235741,0.02160528,0.001638691,0.001586433],"study_design_scores_gemma":[0.0003411708,0.00002542391,0.02483362,0.00001226778,0.00005073613,0.00001408738,0.00007938382,0.9738227,0.00002630795,0.0002544967,0.0002489087,0.0002909716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9402781,0.00000972447,0.008254348,0.0001434981,0.0003417965,0.0003557469,0.0007939655,0.0003916115,0.04943127],"genre_scores_gemma":[0.9701766,0.0008282879,0.02755523,0.0001575229,0.00007641966,0.00006548974,0.0003558625,0.00007053243,0.0007140097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6056602,"threshold_uncertainty_score":0.9999874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860251206476981,"score_gpt":0.2622699100433388,"score_spread":0.243667397978569,"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."}}