{"id":"W2970779754","doi":"10.17077/drivingassessment.1716","title":"Can Virtual Reality Headsets be Used to Measure Accurately Drivers’ Anticipatory Behaviors?","year":2019,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Headset; Virtual reality; Driving simulator; Computer science; Simulation; Duration (music); Driving simulation; Human–computer interaction; Telecommunications","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":[0.002181181,0.0008401897,0.0004766829,0.0009959888,0.0001590731,0.001554191,0.0007417218,0.001127978,0.003714883],"category_scores_gemma":[0.01809647,0.0003821436,0.0006003455,0.0004368022,0.0004075487,0.001815552,0.0007013953,0.0007468253,0.001154431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002351087,"about_ca_system_score_gemma":0.0003915338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003054407,"about_ca_topic_score_gemma":0.004456904,"domain_scores_codex":[0.9990711,0.0004299388,0.00008506729,0.0001446802,0.0001904275,0.00007878062],"domain_scores_gemma":[0.9940972,0.002785298,0.001291251,0.000469944,0.001073009,0.0002832961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002185914,0.0009609867,0.5044747,0.001992366,0.0006703361,0.0002588315,0.003658035,0.004419662,0.02195634,0.001517502,0.006212338,0.451693],"study_design_scores_gemma":[0.0001819477,0.007143961,0.9197407,0.001311257,0.0006346742,0.001429295,0.005401698,0.02419983,0.01468079,0.005804074,0.01905026,0.0004215528],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9323294,0.00657147,0.04744134,0.001802993,0.0006884415,0.0003155556,0.002110847,0.0005774724,0.00816261],"genre_scores_gemma":[0.9758677,0.002976861,0.0188356,0.0004041184,0.0001107036,0.0002327217,0.0005276056,0.00002978119,0.00101477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003714883,"threshold_uncertainty_score":0.01242757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1707590735265984,"score_gpt":0.4331130315406094,"score_spread":0.262353958014011,"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."}}