{"id":"W4230341183","doi":"10.32920/ryerson.14639328","title":"Characterization and Classification Using Autoregressive Modeling and Machine Learning Algorithms","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"University of Toronto","keywords":"Autoregressive model; Naive Bayes classifier; Pattern recognition (psychology); Computer science; Artificial intelligence; Linear discriminant analysis; Gaussian; Classifier (UML); Support vector machine; Algorithm; Nonlinear system; Segmentation; Machine learning; Mathematics; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001954523,0.0009584224,0.001348844,0.001982016,0.0003272862,0.001730323,0.0009939313,0.001050187,0.001353175],"category_scores_gemma":[0.004746601,0.0004801418,0.001409141,0.001834231,0.0005516568,0.001341927,0.0005301349,0.001468489,0.001174428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007357674,"about_ca_system_score_gemma":0.0008728094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004892987,"about_ca_topic_score_gemma":0.002898138,"domain_scores_codex":[0.9989046,0.0003672408,0.00009880448,0.0002464006,0.0003041292,0.00007884399],"domain_scores_gemma":[0.9981284,0.001177295,0.0002254939,0.0001515093,0.0002889218,0.00002847504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008131799,0.000127546,0.002859819,0.0001853862,0.000177013,0.0001359816,0.0001210549,0.6180934,0.007739459,0.0159604,0.002203554,0.3523151],"study_design_scores_gemma":[0.000002569652,0.00001511576,0.0002734272,0.000008071311,0.000009387346,0.0000181632,0.000009430408,0.9939333,0.0008257825,0.004332687,0.0005639422,0.000008215165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005243072,0.0004156745,0.9931331,0.0001343743,0.00002372221,0.00002197416,0.0000470091,0.000576876,0.0004042418],"genre_scores_gemma":[0.2682801,0.001614836,0.7247875,0.0001528539,0.0002472895,0.0002517197,0.0007289618,0.0001897239,0.003747039],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004892987,"threshold_uncertainty_score":0.01033664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03115814742710524,"score_gpt":0.2423972992713271,"score_spread":0.2112391518442219,"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."}}