{"id":"W2604548658","doi":"10.1109/vr.2017.7892253","title":"Lean into it: Exploring leaning-based motion cueing interfaces for virtual reality movement","year":2017,"lang":"en","type":"article","venue":"","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Joystick; Virtual reality; Human–computer interaction; Motion (physics); Computer science; Usability; Interface (matter); Task (project management); Focus (optics); Controllability; Simulation; Artificial intelligence; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0006571504,0.0001389074,0.000146381,0.00006791045,0.0008134771,0.000661907,0.001131551,0.00004165747,0.00001138495],"category_scores_gemma":[0.0002525152,0.0001255489,0.00006655601,0.00006605177,0.00005915736,0.001424789,0.0003202651,0.00008050225,0.00002746257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071265,"about_ca_system_score_gemma":0.00005259117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008543078,"about_ca_topic_score_gemma":0.0003436589,"domain_scores_codex":[0.998812,0.00001950843,0.0002669186,0.0004108983,0.0002202833,0.0002703747],"domain_scores_gemma":[0.9983235,0.00008863469,0.0001848092,0.001175969,0.0001019245,0.0001251571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002228335,0.0001705203,0.0002787153,0.00004186975,0.00002398942,7.978487e-7,0.001965371,0.003698925,0.006695792,0.4628208,0.002327831,0.5219532],"study_design_scores_gemma":[0.001107254,0.0006347189,0.006240075,0.0001195757,0.00001258231,5.935544e-7,0.0007525711,0.7490348,0.1960623,0.03633094,0.00919722,0.0005073586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06132931,0.000004296402,0.9275658,0.007916264,0.0002249719,0.0002899691,0.000002912962,0.0001454683,0.002521056],"genre_scores_gemma":[0.9827862,0.000006734136,0.01594458,0.0007651669,0.00007520295,0.0001282723,0.00000549052,0.000009258892,0.0002790383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9214569,"threshold_uncertainty_score":0.6382784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.157768600860589,"score_gpt":0.3531111444399999,"score_spread":0.1953425435794109,"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."}}