{"id":"W2620760789","doi":"10.3182/20060402-4-br-2902.00699","title":"FAULT DETECTION USING PROJECTION PURSUIT REGRESSION (PPR): A CLASSIFICATION VERSUS AN ESTIMATION BASED APPROACH","year":2006,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Equidistant; Fault detection and isolation; Projection (relational algebra); Fault (geology); Pattern recognition (psychology); Mathematics; Artificial intelligence; Class (philosophy); Regression; Process (computing); Value (mathematics); Computer science; Algorithm; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002844516,0.001362725,0.002667461,0.00126219,0.0004802146,0.001714548,0.001025613,0.002129562,0.001328129],"category_scores_gemma":[0.007558643,0.0004518944,0.0009753527,0.001361462,0.0008567884,0.002103536,0.001148356,0.001894784,0.00083376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002342613,"about_ca_system_score_gemma":0.0007869854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001449815,"about_ca_topic_score_gemma":0.000812033,"domain_scores_codex":[0.9984053,0.0005132807,0.0001318257,0.0003213273,0.0004723579,0.0001559107],"domain_scores_gemma":[0.996313,0.002303865,0.0003454798,0.0003476434,0.000620136,0.00006989176],"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":[0.0008824355,0.0002754692,0.003864454,0.0002829003,0.0002010091,0.0001534305,0.0001118399,0.1208183,0.02820515,0.006702595,0.001993563,0.8365089],"study_design_scores_gemma":[0.00002780958,0.0002343818,0.001320804,0.00002193781,0.00006763941,0.0001722039,0.0000328101,0.98404,0.009997111,0.003469023,0.0005931876,0.00002324885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01720221,0.0003749044,0.9806557,0.000324281,0.00005720091,0.0000396548,0.00002770998,0.000482467,0.0008359422],"genre_scores_gemma":[0.59514,0.00123976,0.3995043,0.0003083209,0.000275187,0.00009352085,0.0001730997,0.0001504153,0.00311536],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002844516,"threshold_uncertainty_score":0.01504338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02539788813597216,"score_gpt":0.2529248738858489,"score_spread":0.2275269857498768,"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."}}