{"id":"W3204730924","doi":"10.3390/modelling2040022","title":"Quantifying the Impact of Inspection Processes on Production Lines through Stochastic Discrete-Event Simulation Modeling","year":2021,"lang":"en","type":"article","venue":"Modelling—International Open Access Journal of Modelling in Engineering Science","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discrete event simulation; Flexibility (engineering); Production line; Computer science; Quality (philosophy); Production (economics); Reliability engineering; Probabilistic logic; Event (particle physics); Industrial engineering; Discrete manufacturing; Productivity; Key (lock); Overall equipment effectiveness; Manufacturing engineering; Engineering; Simulation; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001651631,0.0008810594,0.0006867641,0.0007253388,0.0003846566,0.001409701,0.001070803,0.001101067,0.001334788],"category_scores_gemma":[0.004228111,0.0004378653,0.0009715308,0.000655961,0.0006528766,0.0008842124,0.0007123571,0.0008677213,0.000140947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001543709,"about_ca_system_score_gemma":0.001095121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01246454,"about_ca_topic_score_gemma":0.005082142,"domain_scores_codex":[0.9988412,0.0004969874,0.00005539932,0.0001355795,0.0003212019,0.0001495942],"domain_scores_gemma":[0.9962451,0.002766687,0.0004417706,0.000198302,0.0002521934,0.00009582604],"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":[0.000007365878,0.000008633679,0.0002783962,0.000004791784,0.000003769064,0.000009596805,0.000004806599,0.9978603,0.0001983755,0.0010818,0.00001328175,0.000528849],"study_design_scores_gemma":[0.000001940766,0.000009458874,0.00008946387,0.000001212713,0.000003013408,0.000002491769,0.000002643313,0.9991437,0.0001547589,0.000541867,0.00004757771,0.000001743757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.23315,0.0002291112,0.7576138,0.0002848346,0.00003067541,0.0001709471,0.0003631278,0.0004041457,0.007753441],"genre_scores_gemma":[0.9747193,0.0001681674,0.02353336,0.0000248508,0.000006851086,0.0001117987,0.0001415796,0.00002403748,0.001269998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01246454,"threshold_uncertainty_score":0.02478403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058227445225649,"score_gpt":0.3842283926728565,"score_spread":0.2784056481502916,"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."}}