{"id":"W2026770708","doi":"10.1175/jcli3402.1","title":"A Bayesian Climate Change Detection and Attribution Assessment","year":2005,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Climate variability and models","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Attribution; Climate change; Bayesian probability; Climatology; Forcing (mathematics); Environmental science; Greenhouse gas; Radiative forcing; Climate model; Downscaling; Consistency (knowledge bases); Econometrics; Computer science; Artificial intelligence; Psychology; Mathematics; Social psychology; Ecology; 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":[],"consensus_categories":[],"category_scores_codex":[0.001135598,0.000103843,0.0001811448,0.00004985807,0.0001513551,0.00004499289,0.00008230571,0.00006651869,0.0004428155],"category_scores_gemma":[0.00001563438,0.00008709221,0.00007139488,0.0001121556,0.00006039307,0.0007150687,0.000113698,0.000176854,0.00003963333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002398499,"about_ca_system_score_gemma":0.000004936013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001632341,"about_ca_topic_score_gemma":0.00009165977,"domain_scores_codex":[0.9988589,0.00006810664,0.0003933993,0.0001354867,0.0002623672,0.0002817397],"domain_scores_gemma":[0.9994149,0.00003770083,0.0002944192,0.0001080685,0.00001852091,0.0001264123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000427744,0.0008025105,0.3431993,0.0001375529,0.00005025088,0.00004177697,0.001721111,0.003537835,0.07817356,0.002409995,0.0001072498,0.5693911],"study_design_scores_gemma":[0.002393868,0.000955362,0.8596157,0.0001672562,0.00017801,0.0006659924,0.0002187933,0.08979297,0.003101916,0.001958501,0.04045201,0.0004996232],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887317,0.00006239722,0.006551517,0.001517848,0.0001700682,0.0001457945,0.000013327,0.00001690571,0.002790445],"genre_scores_gemma":[0.9943288,0.002256999,0.002967735,0.0002401007,0.0001860386,0.000005381794,0.000001179125,0.000008586696,0.000005175463],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5688915,"threshold_uncertainty_score":0.4848519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448557405812164,"score_gpt":0.2876382756773627,"score_spread":0.2631527016192411,"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."}}