{"id":"W2996802399","doi":"10.20380/gi2018.17","title":"EZCursorVR: 2D Selection with Virtual Reality Head-Mounted Displays","year":2018,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Joystick; Computer science; Cursor (databases); Virtual reality; Ray casting; Fitts's law; Optical head-mounted display; Input device; Computer vision; Computer graphics (images); Artificial intelligence; Simulation; Computer hardware; Rendering (computer graphics); Volume rendering; Task (project management); Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0002387198,0.000276113,0.0002522255,0.00005155018,0.001783884,0.0002137916,0.002662799,0.0000890065,0.00003482747],"category_scores_gemma":[0.000008869781,0.0002613542,0.0001369571,0.0006618174,0.0004265813,0.0007234829,0.0007694316,0.0004715475,0.00002260943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007520132,"about_ca_system_score_gemma":0.0008050429,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1188365,"about_ca_topic_score_gemma":0.4098305,"domain_scores_codex":[0.998063,0.0002231427,0.0003407874,0.0005268499,0.0003894613,0.0004567125],"domain_scores_gemma":[0.9964281,0.0001748755,0.0002157574,0.002218819,0.0008062845,0.0001561035],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003830151,0.000611203,0.00264551,0.0000385232,0.0007769974,0.000007229871,0.01253439,0.0003236574,0.009366275,0.2940898,0.6742266,0.005341498],"study_design_scores_gemma":[0.002129488,0.001546466,0.06107284,0.0002246535,0.0001076874,0.0001337294,0.001422755,0.6105657,0.00755936,0.0007907409,0.3126896,0.001756964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03295271,0.00005808474,0.9540309,0.005959433,0.0004958856,0.0003714339,0.0000316921,0.0001279347,0.005971937],"genre_scores_gemma":[0.9665866,0.00001468813,0.02846974,0.003935648,0.0002625474,0.00004127637,0.000111509,0.00002275994,0.0005552582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9336339,"threshold_uncertainty_score":0.9999838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02732651498090776,"score_gpt":0.3135927773763946,"score_spread":0.2862662623954868,"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."}}