{"id":"W2599592136","doi":"10.2172/808089","title":"STATISTICAL DAMAGE CLASSIFICATION USING SEQUENTIAL PROBABILITY RATIO TESTS.","year":2002,"lang":"en","type":"report","venue":"","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Response Biomedical (Canada)","funders":"","keywords":"Sequential probability ratio test; Autoregressive model; Statistical hypothesis testing; Normality; Parametric statistics; Classifier (UML); Computer science; Context (archaeology); Probability distribution; Statistics; Statistical inference; Parametric model; Pattern recognition (psychology); Algorithm; Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004312492,0.0003634123,0.0004387811,0.0001374102,0.00008691366,0.00007443762,0.0002052608,0.0005420466,0.0005022197],"category_scores_gemma":[0.0003334057,0.0003554946,0.00006666133,0.0001640758,0.00008125418,0.0001666036,0.00004883009,0.0006804922,0.00002377546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002327466,"about_ca_system_score_gemma":0.000331682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000469294,"about_ca_topic_score_gemma":0.00003149037,"domain_scores_codex":[0.997637,0.00005519122,0.000785543,0.0004140331,0.0007149953,0.0003931917],"domain_scores_gemma":[0.9986734,0.0001263199,0.0001315024,0.0006197969,0.0003067332,0.0001422862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004903146,0.0002809994,0.03561832,0.03319434,0.0006630661,0.0002835643,0.0005319987,0.007818162,0.01831245,0.01299184,0.5067443,0.383512],"study_design_scores_gemma":[0.0005710668,0.0002482236,0.3762999,0.0013567,0.0005518029,0.0004607178,0.00004047178,0.4826123,0.007992739,0.01459386,0.1109339,0.004338334],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2689517,0.001084463,0.4318823,0.0001480798,0.0202058,0.006993052,0.001032427,0.01971501,0.2499871],"genre_scores_gemma":[0.8322064,0.0003109854,0.1641357,0.000009753808,0.00183854,0.0001147192,0.0003198081,0.0001764671,0.000887616],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5632547,"threshold_uncertainty_score":0.9998897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1872594249755853,"score_gpt":0.3874649751849562,"score_spread":0.2002055502093709,"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."}}