{"id":"W3209022538","doi":"10.32920/ryerson.14648091.v1","title":"Intelligent Condition Monitoring Models For Rotating","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Downtime; Condition monitoring; Vibration; Computer science; Condition-based maintenance; Noise (video); Fuzzy logic; Bayesian probability; Function (biology); Artificial intelligence; Machine learning; Reliability engineering; Engineering","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":[0.0001555726,0.0002838095,0.0003162633,0.0001139203,0.00003726175,0.0001577971,0.0002139612,0.0002622587,0.00005944766],"category_scores_gemma":[0.00005177534,0.0003221616,0.0001774609,0.00005125797,0.00000804389,0.0001231908,0.0002242774,0.0004317611,0.000002848581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001450855,"about_ca_system_score_gemma":0.0000221426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006087447,"about_ca_topic_score_gemma":0.000008830254,"domain_scores_codex":[0.9989057,0.00001539336,0.0003687795,0.0003257213,0.0001461036,0.000238269],"domain_scores_gemma":[0.9992788,0.0001335027,0.00005191796,0.0003630414,0.0001126555,0.0000600501],"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":[0.000001536566,0.00003038254,0.0002702316,0.001339337,0.0001232371,0.000004127834,0.0004494026,0.907629,0.0009494889,0.001040449,0.00204304,0.08611974],"study_design_scores_gemma":[0.00005209002,0.00001001981,0.00003251277,0.0005185355,0.0000274557,0.000001183225,0.00006800184,0.8308586,0.1602607,0.007581583,0.0002661719,0.0003231741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06432384,0.0008911744,0.9262215,0.00002614492,0.001079109,0.0008658613,0.00004014601,0.002114677,0.004437504],"genre_scores_gemma":[0.7361397,0.0005371283,0.2609109,0.00001532459,0.0003223144,0.001687803,0.0002414752,0.0000931638,0.00005221612],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6718159,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03810378468685059,"score_gpt":0.3383385042103353,"score_spread":0.3002347195234847,"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."}}