{"id":"W2049311045","doi":"10.1002/cjs.10043","title":"Change detection in linear regression with time series errors","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Series (stratigraphy); Smoothing; Linear regression; Novelty; Statistics; Change detection; Regression; Novelty detection; Computer science; Standardization; Regression analysis; Statistical hypothesis testing; Time series; Mathematics; Algorithm; Artificial intelligence","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":[0.00007862324,0.00007385253,0.000130536,0.0002166446,0.00003455971,0.00001916559,0.00004643617,0.00004412799,0.00002953028],"category_scores_gemma":[0.00002425577,0.00006127515,0.00001334059,0.0001518446,0.00001406115,0.0001245902,4.543176e-7,0.0001566584,0.000007277362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001328992,"about_ca_system_score_gemma":0.00006768388,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007207847,"about_ca_topic_score_gemma":0.04381143,"domain_scores_codex":[0.9995384,0.00001626217,0.0001839326,0.00003743871,0.00008062018,0.000143338],"domain_scores_gemma":[0.9996324,0.000009578955,0.00005310124,0.00004947105,0.00005665795,0.000198745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005935487,0.00004843079,0.005479786,0.0002846398,0.0002122485,0.006703909,0.01141909,0.05633109,0.01726805,0.0007764541,0.01316277,0.88772],"study_design_scores_gemma":[0.006277056,0.005416078,0.1539756,0.00225897,0.000146976,0.004685356,0.002755925,0.6890674,0.00759994,0.001364131,0.1247595,0.001693048],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8662972,0.002578365,0.122511,0.001059926,0.004323265,0.0006617203,0.00031093,0.0001409397,0.002116642],"genre_scores_gemma":[0.9984376,0.00001848277,0.001233331,0.00004152844,0.0001456064,0.000001128041,0.000001490018,0.00001078333,0.0001100145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8860269,"threshold_uncertainty_score":0.9736365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009842939502305184,"score_gpt":0.2016508752485172,"score_spread":0.191807935746212,"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."}}