{"id":"W1974064423","doi":"10.1002/joc.2056","title":"A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series","year":2009,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Institut National de la Recherche Scientifique","funders":"","keywords":"Homogeneity (statistics); Prior probability; Bayesian probability; Series (stratigraphy); Classification of discontinuities; Statistics; Statistical power; Change detection; Computer science; Mathematics; Artificial intelligence; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004194856,0.00006266528,0.0001911105,0.00009146739,0.00004667813,0.0000110708,0.0002794476,0.00005570766,0.0001679254],"category_scores_gemma":[0.0002606908,0.00004398068,0.0001236478,0.000087285,0.0001901735,0.0002296084,0.00002987546,0.0001094221,0.000005301848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004051033,"about_ca_system_score_gemma":0.00001218138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003517463,"about_ca_topic_score_gemma":0.001744711,"domain_scores_codex":[0.9990901,0.00004950969,0.0005021831,0.00007012793,0.0001677775,0.0001203141],"domain_scores_gemma":[0.9991863,0.0003350417,0.0003536616,0.00006446397,0.0000403052,0.00002018647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001549876,0.0006631284,0.8785586,0.00001070647,0.000218403,0.00009192331,0.002030933,0.008022709,0.04558632,0.002667833,0.0001343002,0.06046527],"study_design_scores_gemma":[0.001973424,0.001693853,0.829345,0.00005692978,0.0002735673,0.002046269,0.001467631,0.03975996,0.04966061,0.07126933,0.002140837,0.0003125932],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735634,0.00006863924,0.02147666,0.004144408,0.0002848518,0.00006573623,0.000007500609,0.000002692492,0.0003861445],"genre_scores_gemma":[0.9991736,0.00004398344,0.0005238744,0.0001558221,0.00007300369,0.00000303343,0.00000118585,0.000002428732,0.000023071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0686015,"threshold_uncertainty_score":0.1838665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008542697767515425,"score_gpt":0.2614486854063225,"score_spread":0.2529059876388071,"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."}}