{"id":"W4386736696","doi":"10.1007/978-3-031-43524-9_3","title":"Real-Time KPI Forecasting with 1D Convolutional Time Series for Enhanced Manufacturing Efficiency","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Overall equipment effectiveness; Performance indicator; Mean time between failures; Computer science; Automotive industry; Process (computing); Production (economics); Macro; Quality (philosophy); Reliability engineering; Engineering; Failure rate","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":[0.0002422864,0.000572936,0.0004522796,0.0003494261,0.000122531,0.000518754,0.000447121,0.000328956,0.001784228],"category_scores_gemma":[0.0006962924,0.0001849937,0.0003595896,0.0005740878,0.0001103715,0.0005756469,0.0002693482,0.0005711926,0.0005836232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004237575,"about_ca_system_score_gemma":0.0003327549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005980381,"about_ca_topic_score_gemma":0.007300141,"domain_scores_codex":[0.9999233,0.000009301975,0.000005234902,0.00002053654,0.00002843569,0.00001311811],"domain_scores_gemma":[0.9998666,0.00006211083,0.00001409259,0.0000195336,0.00003254587,0.000005085654],"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":[0.0001693988,0.00007400198,0.0009369383,0.00009117455,0.00005084699,0.00005788309,0.00002241313,0.5531573,0.01974556,0.003716815,0.003923568,0.4180542],"study_design_scores_gemma":[9.75636e-7,0.000007778971,0.0002557714,0.000002443927,0.000004903245,0.000006190212,0.000001252875,0.9969332,0.001973033,0.0004772058,0.000334864,0.000002311876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09117712,0.002028052,0.8963733,0.0003539616,0.0002538809,0.00002281199,0.0005962196,0.002281277,0.00691337],"genre_scores_gemma":[0.8948025,0.001115864,0.09793507,0.00006963041,0.00007624785,0.00002479226,0.0006521616,0.0001129344,0.005210727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005980381,"threshold_uncertainty_score":0.01189113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009213134555718658,"score_gpt":0.1880175089545406,"score_spread":0.178804374398822,"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."}}