{"id":"W2988878602","doi":"10.1145/3359996.3364265","title":"HawKEY: Efficient and Versatile Text Entry for Virtual Reality","year":2019,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Text entry; Computer science; Human–computer interaction; Virtual reality; Visualization; Task (project management); Virtual keyboard; Multimedia; Artificial intelligence; Computer hardware; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006306202,0.001827346,0.0009037235,0.0007356902,0.0003727091,0.001571016,0.001900955,0.001100032,0.02246331],"category_scores_gemma":[0.003461714,0.000577361,0.0005614255,0.0004046803,0.0005388157,0.002265872,0.003102344,0.0006279775,0.00474706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002159715,"about_ca_system_score_gemma":0.0003703544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007199318,"about_ca_topic_score_gemma":0.001316301,"domain_scores_codex":[0.9993985,0.0001209332,0.00004371577,0.0001468106,0.0001961888,0.00009386078],"domain_scores_gemma":[0.9984097,0.0006835118,0.0001119621,0.0003198114,0.0002380073,0.0002368851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002373307,0.0003414249,0.001172015,0.00179129,0.0001259247,0.001530959,0.001560334,0.002062551,0.4910849,0.003228916,0.04167224,0.4530562],"study_design_scores_gemma":[0.00201024,0.01044656,0.03308967,0.0009780271,0.0006677221,0.01200236,0.002255723,0.1239409,0.4409115,0.01169964,0.3604524,0.001545279],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2001536,0.004192469,0.7044238,0.0006115988,0.0008926409,0.0009836089,0.002198854,0.06873687,0.01780658],"genre_scores_gemma":[0.5240522,0.002119976,0.4280398,0.0005464827,0.0003514679,0.0008655197,0.002868591,0.005093559,0.03606248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02246331,"threshold_uncertainty_score":0.07514727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0108014212268782,"score_gpt":0.2541610966973473,"score_spread":0.2433596754704691,"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."}}