{"id":"W2898851764","doi":"10.1109/gem.2018.8516469","title":"Automatic Prediction of Cybersickness for Virtual Reality Games","year":2018,"lang":"en","type":"article","venue":"","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Luonnontieteiden ja Tekniikan Tutkimuksen Toimikunta; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Simulator sickness; Virtual reality; Computer science; Artificial intelligence; Convolutional neural network; Support vector machine; Scratch; Ranking (information retrieval); Recurrent neural network; Ground truth; Machine learning; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004929216,0.0008508093,0.0003483038,0.001128436,0.00009875905,0.0003748601,0.0004169102,0.0004324606,0.001017068],"category_scores_gemma":[0.003505951,0.0002052825,0.0003780718,0.0003279202,0.0001520043,0.0004799393,0.0004296236,0.0005180459,0.0004137146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003564006,"about_ca_system_score_gemma":0.0002934078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003862309,"about_ca_topic_score_gemma":0.006795658,"domain_scores_codex":[0.9995827,0.00009554188,0.0000308236,0.0001305472,0.000104913,0.00005547667],"domain_scores_gemma":[0.9989531,0.0003854353,0.0002104791,0.00008220533,0.0003095688,0.00005918103],"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":[0.001055216,0.0005276735,0.08995415,0.0004509167,0.0002234758,0.0004227951,0.0003047141,0.2312431,0.05863467,0.0008479931,0.004699681,0.6116356],"study_design_scores_gemma":[0.00001212925,0.0001964847,0.04276308,0.00002384323,0.00002452505,0.00009430703,0.00005606878,0.9464403,0.009449719,0.0004430314,0.0004736736,0.0000229198],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7196976,0.0005436114,0.2713084,0.0001555607,0.0001021152,0.0002188608,0.001626758,0.003838749,0.002508273],"genre_scores_gemma":[0.965905,0.0001203689,0.03210622,0.00002584125,0.00001174411,0.0000586151,0.0009880867,0.00004207638,0.00074195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003862309,"threshold_uncertainty_score":0.007679701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03864634498609887,"score_gpt":0.2986466757484058,"score_spread":0.2600003307623069,"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."}}