{"id":"W2305653947","doi":"10.1002/cjs.11282","title":"Consistent two‐stage multiple change‐point detection in linear models","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; York University","funders":"","keywords":"Stage (stratigraphy); Consistency (knowledge bases); Selection (genetic algorithm); Refining (metallurgy); Point (geometry); Change detection; Computer science; Mathematics; Statistics; Algorithm; Applied mathematics; Mathematical optimization; Artificial intelligence; Chemistry; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008378507,0.0001353044,0.0003388186,0.0002505968,0.0000587752,0.00002757746,0.0001474278,0.00006338449,0.0003117381],"category_scores_gemma":[0.006030057,0.00009518288,0.0000509632,0.0001169878,0.0001209986,0.0001262246,0.00001092171,0.0002014203,0.00001216209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002695701,"about_ca_system_score_gemma":0.000399991,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002918716,"about_ca_topic_score_gemma":0.06571461,"domain_scores_codex":[0.998449,0.0001717134,0.0007201409,0.0001247564,0.0002066277,0.000327785],"domain_scores_gemma":[0.9966576,0.001904531,0.000343528,0.0001727161,0.0004006898,0.0005210072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008653061,0.00007253211,0.005053516,0.0001360576,0.00006820346,0.001197074,0.00161962,0.00006657598,0.0009453379,0.5650699,0.002408602,0.423276],"study_design_scores_gemma":[0.002213311,0.000413308,0.005439878,0.0004728847,0.00005633692,0.0001435924,0.000313388,0.02829104,0.0009787891,0.9588788,0.002444653,0.0003539965],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02016704,0.00004564848,0.9777482,0.0002457608,0.000534411,0.000143929,0.0008176282,0.000004414276,0.0002930343],"genre_scores_gemma":[0.6455787,0.00001993647,0.3540545,0.00009809936,0.000109181,0.000004116656,7.193988e-7,0.00001711717,0.0001175295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6254117,"threshold_uncertainty_score":0.9513336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1931860139358327,"score_gpt":0.3397653221315506,"score_spread":0.1465793081957179,"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."}}