{"id":"W4400990378","doi":"10.1002/cjs.11816","title":"Multiple change‐point detection for regression curves","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Regression; Point (geometry); Mathematics; Statistics; Computer science; Geometry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02795525,0.001082498,0.002031837,0.003591022,0.000633455,0.001776944,0.003163936,0.002727075,0.002312296],"category_scores_gemma":[0.1126273,0.0006572495,0.001808141,0.003178958,0.002576626,0.002480989,0.002785582,0.004048554,0.0007281348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135138,"about_ca_system_score_gemma":0.001312531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001746791,"about_ca_topic_score_gemma":0.001271951,"domain_scores_codex":[0.9846987,0.009483748,0.000648069,0.002879259,0.001930002,0.0003601132],"domain_scores_gemma":[0.8930114,0.08531976,0.007440785,0.008121463,0.005269729,0.0008368724],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006751953,0.0003055967,0.02499956,0.0008372911,0.0008541245,0.0004206933,0.0005369126,0.4358231,0.008090056,0.09342056,0.004888671,0.4291481],"study_design_scores_gemma":[0.00002643202,0.000112787,0.004233538,0.00005188808,0.00003057032,0.0001521319,0.00004232817,0.9522706,0.002274908,0.03901974,0.001743384,0.00004169332],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01170679,0.0002558839,0.9871202,0.0001282531,0.00002529106,0.00006272279,0.00007474759,0.0003379573,0.000288161],"genre_scores_gemma":[0.5264887,0.0003345957,0.4692914,0.0001901258,0.000123186,0.0003761357,0.0007359236,0.0004117604,0.002048141],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02795525,"threshold_uncertainty_score":0.1478432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1976636785601183,"score_gpt":0.3750475710278295,"score_spread":0.1773838924677112,"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."}}