{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001749275,0.0001966576,0.0002442274,0.0002248942,0.00003804978,0.00005177181,0.0001407306,0.0002632551,0.000003279229],"category_scores_gemma":[0.0001886806,0.0001622652,0.00002925823,0.0002660242,0.00002187651,0.00009918441,0.0001443053,0.0003594603,1.342899e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000150854,"about_ca_system_score_gemma":0.00002146917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000441438,"about_ca_topic_score_gemma":0.00027032,"domain_scores_codex":[0.999139,0.00002628305,0.0003998711,0.0001976071,0.0001055293,0.0001316794],"domain_scores_gemma":[0.9993984,0.0001424963,0.00008424922,0.000277528,0.00007931302,0.00001803736],"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":[0.000001137193,0.000003494439,0.001506932,0.00009770897,0.00001654521,3.709156e-7,0.0004961464,0.9850816,0.01145934,0.0002339893,0.000001703044,0.001101001],"study_design_scores_gemma":[0.00007320656,0.00001200775,0.004317396,0.0002195788,0.000009445984,1.869308e-7,0.0003704107,0.9710884,0.02348151,0.0002784623,0.00000315695,0.0001462281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3512647,0.0003176097,0.6478646,0.00002400719,0.0001284378,0.0002380178,6.6874e-7,0.0001390213,0.00002284871],"genre_scores_gemma":[0.9845048,0.000108695,0.01524125,6.910302e-7,0.00001785898,0.00003570384,0.00004364704,0.00003363954,0.00001370984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.63324,"threshold_uncertainty_score":0.6616982,"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."}}