{"id":"W6893303774","doi":"10.5281/zenodo.15791779","title":"Harnessing AI Driven Predictive Maintenance: Transforming Manufacturing Efficiency and Reducing Downtime through Advanced Data Analytics","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wycliffe College","funders":"","keywords":"Predictive maintenance; Downtime; Predictive analytics; Data-driven; Process (computing); Resilience (materials science); Decision support system; Big data; Analytics; Manufacturing operations","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":[0.001608332,0.001114763,0.0005528476,0.001692918,0.0004260137,0.002460189,0.001732969,0.001056373,0.001672217],"category_scores_gemma":[0.00549085,0.0003433034,0.0004531795,0.001541741,0.0009256596,0.003139794,0.001428479,0.00130812,0.0006445564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007811275,"about_ca_system_score_gemma":0.000947304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001995928,"about_ca_topic_score_gemma":0.001918875,"domain_scores_codex":[0.9987412,0.0002472488,0.00006778629,0.0002353668,0.0006086135,0.00009980716],"domain_scores_gemma":[0.9955648,0.00208071,0.0005534473,0.001061955,0.0006146861,0.0001245046],"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":[0.0003557444,0.0004015238,0.009706587,0.0007280408,0.0001304404,0.0003745916,0.0005993003,0.2183631,0.04222969,0.02676762,0.008238419,0.6921051],"study_design_scores_gemma":[0.00004863887,0.000244772,0.004368064,0.0001320008,0.00007175039,0.0003716218,0.0003076755,0.8879896,0.03335254,0.05399733,0.01904728,0.0000686788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07347267,0.003329306,0.9061523,0.002510014,0.0002046695,0.0001403517,0.0004494392,0.004697372,0.009043789],"genre_scores_gemma":[0.8134724,0.001647531,0.1813773,0.0003962938,0.0001484183,0.00007871438,0.0006036729,0.0002541469,0.002021454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002460189,"threshold_uncertainty_score":0.008505762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02469736319951796,"score_gpt":0.2695417164763211,"score_spread":0.2448443532768032,"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."}}