{"id":"W4297996452","doi":"10.32920/ryerson.14639736.v2","title":"Virtual Tools for Assessing Human and Organisational Factors in Production System Design","year":2022,"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":"Productivity; Production (economics); Event (particle physics); Work (physics); Virtual actor; Computer science; Human factors and ergonomics; Sensitivity (control systems); Industrial engineering; Systems engineering; Manufacturing engineering; Human–computer interaction; Simulation; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002959685,0.00109389,0.0004570196,0.001354864,0.0003324249,0.002062853,0.000824728,0.0006261563,0.004034903],"category_scores_gemma":[0.009487867,0.0004273266,0.0004556116,0.0005848252,0.0008669845,0.001289129,0.001671385,0.0004751904,0.0003540748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005057739,"about_ca_system_score_gemma":0.0005541006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001162434,"about_ca_topic_score_gemma":0.001021124,"domain_scores_codex":[0.99742,0.001759798,0.00009397104,0.0001304114,0.0005263971,0.00006954087],"domain_scores_gemma":[0.9924281,0.005969507,0.0004106115,0.0007285815,0.0002739973,0.000189179],"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":[0.001083283,0.0005066681,0.01234944,0.0008356776,0.0002870591,0.0003221392,0.002716862,0.6669136,0.03132883,0.0460726,0.001932381,0.2356515],"study_design_scores_gemma":[0.0002632309,0.001754642,0.01116585,0.0003460125,0.0001523622,0.0002881458,0.00149054,0.8844808,0.02111401,0.05502963,0.02371618,0.0001985689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1452114,0.0004231177,0.8424961,0.0001413194,0.00006327756,0.0002490754,0.0003019267,0.001162747,0.009951143],"genre_scores_gemma":[0.7308353,0.0002417415,0.2671795,0.00003800733,0.00001718656,0.0004167246,0.0001634752,0.00008425173,0.001023769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004034903,"threshold_uncertainty_score":0.01565248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06950864626128199,"score_gpt":0.2642873645354122,"score_spread":0.1947787182741302,"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."}}