{"id":"W4280546078","doi":"10.1109/syscon53536.2022.9773846","title":"Analyzing and Predicting Overall Equipment Effectiveness in Manufacturing Industries using Machine Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Systems Conference (SysCon)","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Metric (unit); Overall equipment effectiveness; Factory (object-oriented programming); Computer science; Context (archaeology); Machine learning; Process (computing); Artificial intelligence; Production (economics); Product (mathematics); Manufacturing; Industrial engineering; Data mining; Engineering; Mathematics","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":[0.001390603,0.0008139928,0.0007921638,0.002050681,0.0002147069,0.0007522244,0.0006469283,0.0008012945,0.0003567279],"category_scores_gemma":[0.004482044,0.0002558564,0.0005244046,0.001177139,0.000363107,0.001241153,0.0004045478,0.000502614,0.0001904945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005937172,"about_ca_system_score_gemma":0.0003999812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002580885,"about_ca_topic_score_gemma":0.002501115,"domain_scores_codex":[0.9991297,0.0002078436,0.0000756088,0.0001777093,0.0003295309,0.0000795348],"domain_scores_gemma":[0.9973826,0.001591555,0.0004472107,0.000204728,0.0003135933,0.00006032947],"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":[0.00007333162,0.0001438376,0.01876956,0.00007798239,0.00004376026,0.00005910715,0.00003914609,0.9073402,0.004145887,0.0004867698,0.0001264319,0.06869393],"study_design_scores_gemma":[0.000001508733,0.00005406454,0.004085671,0.000004141476,0.000004515562,0.00001468158,0.00001081674,0.9932448,0.002106548,0.0003923117,0.0000761826,0.00000474608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4069391,0.0004703201,0.5907151,0.000090875,0.00001414109,0.00006385031,0.0001592571,0.0005450728,0.001002233],"genre_scores_gemma":[0.9516677,0.0001223521,0.0475623,0.00001357251,0.000007549716,0.00004079455,0.0002412726,0.0000166604,0.0003277922],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002580885,"threshold_uncertainty_score":0.007354319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03607803874613207,"score_gpt":0.2618467685792952,"score_spread":0.2257687298331632,"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."}}