{"id":"W2207162230","doi":"10.1115/omae2015-41132","title":"Achieving Competence in Offshore Emergency Egress Using Virtual Environment Training","year":2015,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Atlantic Canada Opportunities Agency","keywords":"Trainer; Competence (human resources); Computer science; Virtual training; Emergency response; Transfer of training; Training (meteorology); Virtual machine; Virtual reality; Knowledge management; Human–computer interaction; Psychology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003288674,0.0001124837,0.0001486135,0.0001223456,0.00005595901,0.00001252339,0.0001278799,0.00006461408,0.0203225],"category_scores_gemma":[0.00002713615,0.0001075792,0.00004335372,0.00008928528,0.00003244794,0.0001451794,0.00003856765,0.0001740947,0.0007646051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001046478,"about_ca_system_score_gemma":0.00002208588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001029846,"about_ca_topic_score_gemma":0.00006042772,"domain_scores_codex":[0.9988308,0.000147515,0.0003891011,0.0002333742,0.0001776398,0.0002215801],"domain_scores_gemma":[0.9995527,0.00003375075,0.0000892164,0.0001977161,0.00001793688,0.000108658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004598895,0.0014689,0.2999879,0.00002709617,0.0002570466,0.0002122633,0.338234,0.02234829,0.007088546,0.1789044,0.01817229,0.1328394],"study_design_scores_gemma":[0.004524978,0.0004885198,0.5973959,0.0001419826,0.00004103232,0.0002009466,0.1959121,0.07758053,0.0001592459,0.0005683824,0.1216171,0.001369247],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8764833,0.00004748148,0.01168558,0.0002436253,0.001482954,0.0001152707,0.000002724403,0.00007613911,0.1098629],"genre_scores_gemma":[0.9943717,0.000004030837,0.001008276,0.0001559594,0.0000970414,0.00001094512,0.00000601835,0.00001307056,0.00433295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.297408,"threshold_uncertainty_score":0.9827707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1759304126416349,"score_gpt":0.3964107671068834,"score_spread":0.2204803544652486,"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."}}