{"id":"W2796178","doi":"10.32920/ryerson.14639736","title":"Virtual Tools for Assessing Human and Organisational Factors in Production System Design","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Ergonomics and Human Factors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Production (economics); Productivity; Virtual actor; Event (particle physics); Work (physics); Computer science; Human factors and ergonomics; Sensitivity (control systems); Industrial engineering; Systems engineering; Human–computer interaction; Manufacturing engineering; Engineering; Simulation; Virtual reality; Poison control; Mechanical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001965288,0.0001951751,0.0002547709,0.000127841,0.0000741513,0.0005562856,0.00007278382,0.0001822873,0.00002533088],"category_scores_gemma":[0.0000361855,0.0002050628,0.00004249684,0.00003110452,0.00001378776,0.0002456598,0.0000704751,0.000224524,5.936895e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003451991,"about_ca_system_score_gemma":0.00007782083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002490568,"about_ca_topic_score_gemma":0.00005361013,"domain_scores_codex":[0.9991642,0.00002271476,0.0002934449,0.0003059967,0.00007337231,0.0001403155],"domain_scores_gemma":[0.9996271,0.00009488579,0.00004666063,0.0001533957,0.00004217739,0.00003579738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001035254,0.00007712212,0.008599568,0.003063797,0.00033837,0.000005468125,0.006105114,0.8975759,0.05228185,0.02845162,0.0006305528,0.002860254],"study_design_scores_gemma":[0.002435911,0.0002073444,0.4365708,0.003798118,0.0003520355,0.00002879397,0.05011554,0.2845362,0.2123612,0.003277138,0.00064482,0.005672022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279119,0.00004514098,0.07033969,0.000006539265,0.0008405104,0.000432608,0.000009633525,0.0001679235,0.0002460649],"genre_scores_gemma":[0.9955572,0.000007364359,0.003885236,0.000002073468,0.000182291,0.00005028017,0.0002037847,0.00004532276,0.00006638272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6130397,"threshold_uncertainty_score":0.8362219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06573363098982592,"score_gpt":0.2574210648669073,"score_spread":0.1916874338770814,"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."}}