{"id":"W4280630276","doi":"10.1175/jcli-d-21-0811.1","title":"Multi-Model Forecast Quality Assessment of CMIP6 Decadal Predictions","year":2022,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos","funders":"Horizon 2020 Framework Programme; European Centre for Medium-Range Weather Forecasts; European Commission; H2020 Marie Skłodowska-Curie Actions; “la Caixa” Foundation","keywords":"Coupled model intercomparison project; Initialization; Climatology; Precipitation; Forecast skill; Environmental science; Climate model; Quality (philosophy); Computer science; Ensemble forecasting; Meteorology; Climate change; Geography; Machine learning; Geology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002220163,0.00009330655,0.0002637919,0.00005393923,0.0002376526,0.0000125909,0.0002772257,0.0000326773,0.001648994],"category_scores_gemma":[0.00005048195,0.00008341523,0.0001891495,0.0001504585,0.00009758543,0.0002515157,0.0003917125,0.0003133538,0.000003741555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000333372,"about_ca_system_score_gemma":0.00005846144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005630398,"about_ca_topic_score_gemma":0.00004772002,"domain_scores_codex":[0.9980576,0.0001664881,0.0007754506,0.0001373205,0.000642058,0.0002211005],"domain_scores_gemma":[0.9989278,0.0001025434,0.0006283633,0.0002052268,0.00002977504,0.0001062396],"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":[0.00008467053,0.0008864604,0.1064003,0.0000254829,0.00002880765,0.000005330596,0.0005894588,0.8748266,0.01520282,0.0009441549,0.0003572213,0.000648776],"study_design_scores_gemma":[0.001418271,0.0004239843,0.1724857,0.00001690374,0.00007791866,0.00008431244,0.0006068515,0.8206829,0.0002360542,0.00177839,0.002019484,0.0001692258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697969,0.00001159499,0.02391477,0.0003732253,0.0002607667,0.0001262076,0.0002061289,0.00001029038,0.005300114],"genre_scores_gemma":[0.9784641,0.00009714488,0.0212287,0.00009768763,0.00002015076,0.000008277227,0.000004254764,0.000009345198,0.00007029642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06608543,"threshold_uncertainty_score":0.9992636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06981979827860825,"score_gpt":0.3561534018987703,"score_spread":0.2863336036201621,"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."}}