{"id":"W4405933084","doi":"10.35784/acs-2024-37","title":"STUDY ON DEEP LEARNING MODELS FOR THE CLASSIFICATION OF VR SICKNESS LEVELS","year":2024,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Optech (Canada)","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Motion sickness; Simulator sickness; Virtual reality; Artificial intelligence; Motion (physics); Deep learning; Sensory system; Machine learning; Computer vision; Human–computer interaction; Cognitive psychology","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":[0.001045702,0.0008894386,0.000561304,0.0004652532,0.0002425379,0.0007757953,0.000965727,0.0009640151,0.001130674],"category_scores_gemma":[0.002400661,0.0002998326,0.0006965502,0.0004685317,0.0002717639,0.001042392,0.0004737875,0.001667683,0.0002721588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007570256,"about_ca_system_score_gemma":0.0008587239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01092266,"about_ca_topic_score_gemma":0.005838593,"domain_scores_codex":[0.9997242,0.00006328096,0.00001977583,0.00008345323,0.00006052211,0.00004874469],"domain_scores_gemma":[0.9991948,0.0003883615,0.00005523552,0.00004717026,0.0002766437,0.00003772876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002146091,0.0002643603,0.006022402,0.0001453163,0.000195982,0.00007813206,0.000104703,0.7439888,0.006116882,0.004794104,0.001740728,0.2363339],"study_design_scores_gemma":[0.000002156864,0.00003285065,0.0002240388,0.000006734011,0.00001147072,0.000005878552,0.000004887165,0.9987295,0.0004539168,0.0003708732,0.0001553608,0.00000235358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1863928,0.004919737,0.8010418,0.001295996,0.0001983832,0.00009431162,0.0001602657,0.0009550761,0.004941696],"genre_scores_gemma":[0.9378561,0.001447986,0.0561553,0.000303426,0.00006098509,0.00008672228,0.0002933161,0.00003650668,0.003759704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01092266,"threshold_uncertainty_score":0.0217182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179226959515857,"score_gpt":0.3280148382512142,"score_spread":0.2100921422996286,"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."}}