{"id":"W4312794089","doi":"10.1109/ismar55827.2022.00099","title":"Touching The Droid: Understanding and Improving Touch Precision With Mobile Devices in Virtual Reality","year":2022,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Rendering (computer graphics); Virtual reality; Offset (computer science); Mobile device; Computer vision; Representation (politics); Immersion (mathematics); Augmented reality; Human–computer interaction; Artificial intelligence; Computer graphics (images); 3D interaction; Interaction technique; Gesture; Mathematics","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":[0.0007990001,0.0007816532,0.0004214584,0.0004713547,0.0002214922,0.001654164,0.0007361629,0.0008627446,0.001531923],"category_scores_gemma":[0.007269072,0.0004949268,0.0003490464,0.0003184537,0.0006133062,0.002381022,0.001604266,0.0006374658,0.0002672032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002827192,"about_ca_system_score_gemma":0.0002616704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009999122,"about_ca_topic_score_gemma":0.001291118,"domain_scores_codex":[0.9991775,0.0002733938,0.00005108099,0.000152659,0.0002640739,0.00008127128],"domain_scores_gemma":[0.9977914,0.001442475,0.0002436827,0.0002907391,0.0001844803,0.00004713969],"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":[0.0006861316,0.0002148854,0.00715224,0.001133362,0.00008332974,0.0004515167,0.004721595,0.02342308,0.4589668,0.00370253,0.0007500628,0.4987144],"study_design_scores_gemma":[0.0002017449,0.003968427,0.1045223,0.0006697577,0.0004169197,0.005337516,0.003823325,0.4074306,0.442079,0.01072295,0.02018931,0.0006381333],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4503642,0.00226672,0.5426634,0.0004030468,0.00003989581,0.0001151191,0.0001000101,0.0009066259,0.00314101],"genre_scores_gemma":[0.8466363,0.0008781942,0.1513632,0.0001079908,0.00001973626,0.00004539571,0.00005564674,0.0001020286,0.000791324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001654164,"threshold_uncertainty_score":0.005124807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02308857505476281,"score_gpt":0.2604149088332637,"score_spread":0.2373263337785009,"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."}}