{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04081765,0.001157154,0.001764966,0.00800179,0.001816712,0.005543765,0.002878471,0.002948116,0.008257075],"category_scores_gemma":[0.1655011,0.001006553,0.002101907,0.00424055,0.004064442,0.006124307,0.004429371,0.002755685,0.0005201569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002824302,"about_ca_system_score_gemma":0.002764413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004876496,"about_ca_topic_score_gemma":0.00325597,"domain_scores_codex":[0.9749101,0.01357563,0.001390915,0.002737879,0.006708677,0.0006768081],"domain_scores_gemma":[0.8796458,0.1005984,0.006024806,0.003531867,0.009023379,0.001175685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009091434,0.0002394833,0.03474969,0.0005234227,0.0007268051,0.0004877235,0.0007481687,0.3417279,0.001443086,0.3795794,0.004648572,0.2342167],"study_design_scores_gemma":[0.00008061031,0.00008646872,0.006050518,0.0001384472,0.0001604565,0.0001666245,0.0001196631,0.6439479,0.0008131185,0.3455856,0.002757571,0.00009302147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0866807,0.0007985392,0.8950678,0.002523501,0.0001026934,0.0003119187,0.0008106937,0.0003219433,0.01338212],"genre_scores_gemma":[0.7817765,0.0003795254,0.2140677,0.0003454992,0.0002866382,0.0002762411,0.000788601,0.00007826745,0.002000943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04081765,"threshold_uncertainty_score":0.2158669,"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."}}