{"id":"W7125477664","doi":"10.1109/ciscon66933.2025.11337619","title":"Smart Predictive Maintenance: ML-Driven Fault Detection & Dashboard Insights","year":2025,"lang":"","type":"article","venue":"","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Fault detection and isolation; Troubleshooting; Fault (geology); Key (lock); Measure (data warehouse); Context (archaeology)","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":[0.00026782,0.0008666374,0.0008136606,0.0008427124,0.0003196714,0.0002189343,0.0006758402,0.0007471623,0.0007168151],"category_scores_gemma":[0.0004823155,0.0008587627,0.000366085,0.00135189,0.0001826283,0.0005797219,0.0003794342,0.001251204,0.0005153051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033223,"about_ca_system_score_gemma":0.0001154652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007281839,"about_ca_topic_score_gemma":0.002688787,"domain_scores_codex":[0.9962738,0.0001840458,0.001070814,0.001063155,0.0004923982,0.0009158411],"domain_scores_gemma":[0.9976529,0.0003372327,0.0001543544,0.001207521,0.0003989328,0.0002490792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008804463,0.002126529,0.01813116,0.003648859,0.004366991,0.0001171497,0.006488897,0.06824381,0.1042794,0.03321111,0.377149,0.3813567],"study_design_scores_gemma":[0.001605259,0.0003432416,0.01174213,0.001415491,0.0003762172,0.00001254056,0.0002263738,0.3429167,0.1474071,0.004690242,0.4879289,0.001335855],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08154111,0.0008827168,0.6770253,0.0005840723,0.003232951,0.00229236,0.00004995357,0.0045529,0.2298387],"genre_scores_gemma":[0.9792014,0.002025684,0.00803716,0.0004693385,0.0002570894,0.0006585331,0.0000417746,0.0001184222,0.009190586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8976603,"threshold_uncertainty_score":0.9993863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007132621188200915,"score_gpt":0.2667281205966406,"score_spread":0.2595954994084397,"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."}}