{"id":"W4410599672","doi":"10.14419/05tz0p10","title":"Machine learning-based predictive maintenance: enhancing industrial reliability through data-driven approaches","year":2025,"lang":"en","type":"article","venue":"International Journal of Basic and Applied Sciences","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Reliability (semiconductor); Predictive maintenance; Reliability engineering; Machine learning; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.0009633682,0.0001218358,0.0001994556,0.000158608,0.00009109953,0.000101925,0.0007983059,0.00006448608,0.00001958741],"category_scores_gemma":[0.0002682094,0.00009453038,0.0000385756,0.0001987062,0.0002577428,0.000326633,0.0001388024,0.0004076723,7.285593e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008003195,"about_ca_system_score_gemma":0.0001160424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001836949,"about_ca_topic_score_gemma":0.00001191551,"domain_scores_codex":[0.9988146,0.00003383362,0.0003879061,0.0002096604,0.0004154982,0.0001384746],"domain_scores_gemma":[0.999335,0.0002762619,0.0001486549,0.0001174411,0.00008554916,0.00003711079],"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":[0.000418411,0.0003854948,0.06144315,0.0001134605,0.0004746776,0.00002518814,0.00101018,0.8148866,0.003326806,0.0212441,0.01498042,0.08169151],"study_design_scores_gemma":[0.00223598,0.0002892252,0.004097905,0.0005948873,0.00008977133,0.00002799599,0.0007596939,0.9143043,0.0393776,0.01188974,0.02593181,0.0004010929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.51478,0.0008409816,0.4451215,0.003571949,0.001863048,0.0004778962,0.0001201843,0.0002863719,0.03293808],"genre_scores_gemma":[0.9928196,0.0001059362,0.006739277,0.0001157645,0.0001793395,0.000008163884,0.00001017917,0.000005791457,0.00001592777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4780397,"threshold_uncertainty_score":0.3854837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04241981567101291,"score_gpt":0.2997693912834461,"score_spread":0.2573495756124332,"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."}}