{"id":"W4285464611","doi":"10.32920/ryerson.14665512.v1","title":"The Integration of Human Factors into Discrete Event Simulation and Technology Acceptance in Engineering Design","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs; Workplace Safety and Insurance Board","keywords":"Discrete event simulation; Event (particle physics); Quality (philosophy); Computer science; Work (physics); Action (physics); Test (biology); Risk analysis (engineering); Industrial engineering; Knowledge management; Engineering; Operations management; Business; Simulation; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.005484203,0.0007588931,0.0004487776,0.0006314811,0.0004082193,0.001943464,0.0008249961,0.0008004809,0.003061425],"category_scores_gemma":[0.02062182,0.0004589483,0.0006832799,0.0004845612,0.0009516643,0.001488324,0.0009448513,0.001116081,0.0002860691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001660582,"about_ca_system_score_gemma":0.002167796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01179969,"about_ca_topic_score_gemma":0.008459602,"domain_scores_codex":[0.9947152,0.004201233,0.0001127479,0.0002000809,0.0006348457,0.0001359261],"domain_scores_gemma":[0.9759219,0.02089394,0.0009684864,0.0006879166,0.001243454,0.0002842984],"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":[0.0004202972,0.0008105368,0.01737025,0.0002827905,0.0001943385,0.00009936654,0.001906458,0.8519024,0.003041385,0.03287577,0.0004402516,0.09065608],"study_design_scores_gemma":[0.00005606029,0.0007503155,0.002836833,0.0000664484,0.00004625651,0.00002745193,0.0004052262,0.982653,0.001866638,0.008779188,0.002469205,0.00004330269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2077411,0.0001862366,0.7773398,0.0008131783,0.00009516901,0.0004666214,0.00005783058,0.0002528928,0.01304716],"genre_scores_gemma":[0.8715404,0.0001737651,0.1255908,0.00006841828,0.00001973824,0.0003292341,0.00003022918,0.00003226738,0.002215151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01179969,"threshold_uncertainty_score":0.02900362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593355174166693,"score_gpt":0.2734494164014336,"score_spread":0.2575158646597667,"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."}}