{"id":"W3085790605","doi":"10.1029/2020ef001667","title":"Robustness of CMIP6 Historical Global Mean Temperature Simulations: Trends, Long‐Term Persistence, Autocorrelation, and Distributional Shape","year":2020,"lang":"en","type":"article","venue":"Earth s Future","topic":"Climate variability and models","field":"Environmental Science","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canmore Museum and Geoscience Centre; Global Institute for Water Security; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Global Water Futures; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Pacific Institute for the Mathematical Sciences; Division of Mathematical Sciences; National Science Foundation","keywords":"Climatology; Coupled model intercomparison project; Climate change; Environmental science; Autocorrelation; Term (time); Global warming; Climate model; Robustness (evolution); Proxy (statistics); Econometrics; Statistics; Mathematics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006745604,0.0001286167,0.0001605336,0.00001185933,0.0001480603,0.0000220332,0.0001145187,0.0001435,0.002471032],"category_scores_gemma":[0.00003131205,0.0001148075,0.00006839183,0.0003713253,0.0001034571,0.0002052889,0.00008947653,0.0001495859,0.000008258861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001296442,"about_ca_system_score_gemma":0.00001692191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001479603,"about_ca_topic_score_gemma":0.00008619553,"domain_scores_codex":[0.9990017,0.00004229355,0.0002070188,0.0003177254,0.000277022,0.0001542097],"domain_scores_gemma":[0.9995933,0.0000277059,0.0000702804,0.0001358611,0.00001777682,0.0001550646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001594394,0.0002473697,0.7274708,0.00008105244,0.00003905674,0.00000846073,0.001581101,0.2541077,0.001038003,0.002243473,0.005442212,0.007581297],"study_design_scores_gemma":[0.0004824525,0.00008640462,0.8728361,0.00001308706,0.00003965493,0.00001080238,0.00007080266,0.1191701,0.00001688308,0.00006596999,0.007001051,0.000206756],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919272,0.0001960486,0.001985389,0.004383193,0.0002365442,0.0001810086,0.0003844058,0.00005072958,0.0006554627],"genre_scores_gemma":[0.9987488,0.00001047469,0.0005495785,0.000144078,0.000219578,0.000003374128,0.0001870058,0.000004811743,0.0001322891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1453653,"threshold_uncertainty_score":0.9984409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01982513349283871,"score_gpt":0.2244222623394728,"score_spread":0.2045971288466341,"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."}}