{"id":"W2889723682","doi":"10.1002/joc.5846","title":"Reliability of climate model multi‐member ensembles in estimating internal precipitation and temperature variability at the multi‐decadal scale","year":2018,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Climate variability and models","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Cru; Climatology; Precipitation; Environmental science; Climate model; Downscaling; Reliability (semiconductor); Climate change; Scale (ratio); Meteorology; Geography; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01012636,0.001018524,0.001146319,0.001389908,0.0005998233,0.00164972,0.0008497309,0.0009462703,0.0003722505],"category_scores_gemma":[0.02024617,0.0005186378,0.001615288,0.001082587,0.0003467053,0.001830941,0.001383166,0.001162041,0.0002181443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005682389,"about_ca_system_score_gemma":0.0007636884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008927671,"about_ca_topic_score_gemma":0.005471503,"domain_scores_codex":[0.9979895,0.001117081,0.0001722678,0.000320782,0.0002691543,0.0001311992],"domain_scores_gemma":[0.9862001,0.00784779,0.001275919,0.002501055,0.001810908,0.0003641624],"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":[0.0005061873,0.00009131357,0.09773485,0.00008279751,0.000872795,0.00010333,0.0001946305,0.8604262,0.002003448,0.0008836836,0.0007186257,0.03638214],"study_design_scores_gemma":[0.00001525405,0.00007130701,0.02751635,0.00003186329,0.000125393,0.00003514016,0.00006943109,0.969255,0.001419084,0.000972995,0.0004354662,0.0000528016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9007321,0.00107213,0.09406688,0.0002293355,0.0001420105,0.00008092394,0.001104814,0.0006019239,0.001969791],"genre_scores_gemma":[0.9865797,0.0001880775,0.01187346,0.00002864142,0.00003349361,0.00003361662,0.001064351,0.0000579229,0.000140675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01012636,"threshold_uncertainty_score":0.05355394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891713078161483,"score_gpt":0.3101011895181501,"score_spread":0.2911840587365353,"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."}}