{"id":"W4243444149","doi":"10.32920/ryerson.14648130","title":"Virtual Human Factors Tools for Proactive Ergonomics: Qualitative Exploration And Method Development","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Flexibility (engineering); Context (archaeology); Computer science; Presentation (obstetrics); Motion capture; Human–computer interaction; Human factors and ergonomics; Graphics; Motion (physics); Knowledge management; Process management; Systems engineering; Engineering; Artificial intelligence; Poison control","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007702896,0.0003347782,0.0004698624,0.0002043764,0.0002514556,0.0003360866,0.0001367385,0.000352365,0.002933946],"category_scores_gemma":[0.0001933453,0.0003220947,0.0001409678,0.00004722951,0.00004272518,0.0004708339,0.0001865011,0.0004028536,0.00004189638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002717554,"about_ca_system_score_gemma":0.0001826724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009083956,"about_ca_topic_score_gemma":0.0002097481,"domain_scores_codex":[0.9974663,0.0006806271,0.0007385131,0.0007638314,0.0001377355,0.0002129546],"domain_scores_gemma":[0.9978052,0.001065992,0.0004166112,0.0003022978,0.0003153827,0.00009455927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002293826,0.0002686871,0.00001920615,0.00007449402,0.0013353,0.000001276678,0.8471317,0.0001515528,0.0003492602,0.1018257,0.002226015,0.04638749],"study_design_scores_gemma":[0.001563272,0.0002457926,0.007516052,0.000104576,0.000107549,0.00000393948,0.95056,0.0007305544,0.01153046,0.00447077,0.02207561,0.001091392],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3058881,0.00001845627,0.6797799,0.0002477934,0.001479122,0.001346186,0.00005763342,0.0001647218,0.01101811],"genre_scores_gemma":[0.8824286,0.00000870071,0.09208485,0.0003390397,0.0002238911,0.002811124,0.003068354,0.00007630116,0.01895911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.587695,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2592144776950634,"score_gpt":0.5006554483344278,"score_spread":0.2414409706393644,"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."}}