{"id":"W3091879132","doi":"10.1007/978-3-030-59990-4_24","title":"Text Input in Virtual Reality Using a Tracked Drawing Tablet","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Stylus; Words per minute; Computer science; Tablet pc; Text entry; Virtual reality; Controller (irrigation); Test (biology); Virtual keyboard; Human–computer interaction; Computer graphics (images); Simulation; Multimedia; Computer hardware; Computer vision; Reading (process)","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":[0.0003099169,0.001027905,0.0007690775,0.0004005771,0.000464897,0.001759734,0.001652067,0.001213081,0.04374934],"category_scores_gemma":[0.001275073,0.0005672676,0.000581737,0.0006255535,0.0003895402,0.001451822,0.001548498,0.0005506526,0.005477219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002319163,"about_ca_system_score_gemma":0.0001764436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00122988,"about_ca_topic_score_gemma":0.001191116,"domain_scores_codex":[0.9995683,0.0001217255,0.0000251814,0.00009226244,0.0001413203,0.00005127172],"domain_scores_gemma":[0.999508,0.0003276473,0.00001483011,0.00006200087,0.00005572816,0.00003176031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003325287,0.000225788,0.0005655732,0.001040955,0.00008364097,0.001551743,0.001793429,0.01266433,0.4152785,0.009865385,0.01910234,0.5345029],"study_design_scores_gemma":[0.000948313,0.003153988,0.01084146,0.0006416056,0.0004441382,0.00683563,0.002272694,0.3124395,0.4858673,0.01245876,0.1635395,0.0005571007],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1002098,0.001606695,0.8292526,0.0002252208,0.0003099031,0.0002237919,0.0008205611,0.01366257,0.05368885],"genre_scores_gemma":[0.6431357,0.002012835,0.2607634,0.0002368333,0.000158593,0.0003299447,0.001212852,0.00176372,0.09038614],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04374934,"threshold_uncertainty_score":0.1463561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03177309587923935,"score_gpt":0.2790113133090684,"score_spread":0.2472382174298291,"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."}}