{"id":"W4399898394","doi":"10.1016/j.ijdrr.2024.104630","title":"Exploring the most effective feedback system for training people in Earthquake emergency preparedness using immersive virtual reality serious games","year":2024,"lang":"en","type":"article","venue":"International Journal of Disaster Risk Reduction","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Virtual reality; Preparedness; Training (meteorology); Serious game; Emergency management; Computer science; First responder; Multimedia; Computer security; Human–computer interaction; Medical emergency; Medicine","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":[0.000360507,0.0007041647,0.0002781285,0.0003414343,0.0002011443,0.0006742812,0.0007292997,0.0004931791,0.006112966],"category_scores_gemma":[0.001839558,0.0001844436,0.0002673008,0.00009117427,0.0001367227,0.0008098716,0.0005402783,0.0003320359,0.0005070746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319305,"about_ca_system_score_gemma":0.000397656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001259297,"about_ca_topic_score_gemma":0.001689403,"domain_scores_codex":[0.9998022,0.00006129248,0.000009922896,0.00003375572,0.00004706032,0.00004582314],"domain_scores_gemma":[0.9996226,0.0002125861,0.00002510271,0.000009300384,0.00007611713,0.00005426623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004893659,0.005747596,0.01161469,0.003563059,0.000206286,0.000876077,0.003691851,0.02708036,0.2874932,0.003834246,0.008860933,0.6421381],"study_design_scores_gemma":[0.002131578,0.02548807,0.07953306,0.001340846,0.001702093,0.001427328,0.01065081,0.5993546,0.2286114,0.008227748,0.04106007,0.0004723537],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.758091,0.0006700842,0.2249606,0.0005771353,0.0001934341,0.0009328746,0.0002480914,0.002888727,0.01143802],"genre_scores_gemma":[0.9147438,0.0003557768,0.08110218,0.0001512129,0.00001568238,0.0003542163,0.0001029142,0.00005917891,0.003115054],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006112966,"threshold_uncertainty_score":0.02044988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03640164994806672,"score_gpt":0.290435561136655,"score_spread":0.2540339111885883,"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."}}