{"id":"W2809869736","doi":"10.5194/esd-2018-51","title":"Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing","year":2018,"lang":"en","type":"article","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos; Université du Québec à Montréal","funders":"Global Change Institute, University of the Witwatersrand, Johannesburg; National Science Foundation; Climate Extremes; Australian Research Council; European Commission; National Center for Atmospheric Research","keywords":"Weighting; Climate model; Computer science; Coupled model intercomparison project; Model selection; Range (aeronautics); Econometrics; Representation (politics); Selection (genetic algorithm); Sample (material); Climate change; Machine learning; Mathematics; Ecology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.204006,0.001660641,0.003015824,0.002349132,0.00230214,0.003650306,0.005567627,0.00234164,0.003467984],"category_scores_gemma":[0.463883,0.0006655536,0.004498527,0.00317426,0.004833682,0.007288467,0.005586091,0.006451347,0.0003803504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001509377,"about_ca_system_score_gemma":0.002462337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004317581,"about_ca_topic_score_gemma":0.003938137,"domain_scores_codex":[0.8546553,0.1235149,0.004027195,0.008786574,0.007665771,0.001350203],"domain_scores_gemma":[0.3420572,0.59429,0.01111843,0.0383313,0.01178603,0.002417084],"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":[0.003964464,0.001153953,0.2931836,0.001044272,0.0150336,0.001174993,0.002313384,0.3125826,0.002368056,0.1213396,0.01063372,0.2352079],"study_design_scores_gemma":[0.0002821389,0.0008061202,0.0169385,0.0002545459,0.0008975981,0.0001407788,0.0005441662,0.8538328,0.002855833,0.119856,0.003486613,0.0001048369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2747994,0.00122863,0.7140403,0.003073196,0.0005598221,0.0005527347,0.0005017103,0.0008173082,0.004426934],"genre_scores_gemma":[0.8986633,0.0002121363,0.09799771,0.0009284166,0.000224935,0.0004738827,0.0007748664,0.000247768,0.0004769827],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.204006,"threshold_uncertainty_score":0.9816024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08787898312181114,"score_gpt":0.294700129875702,"score_spread":0.2068211467538908,"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."}}