{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003972494,0.00008308925,0.00008617404,0.0000728774,0.0004437822,0.0001491641,0.0003835471,0.00001235552,0.0000334001],"category_scores_gemma":[0.00001430444,0.00005292095,0.00001862766,0.00025689,0.00002702555,0.0006199797,0.0005452887,0.0002156693,0.000001199969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001824285,"about_ca_system_score_gemma":0.00003787811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005432158,"about_ca_topic_score_gemma":0.0003790056,"domain_scores_codex":[0.999061,0.000126944,0.0001215964,0.0002872674,0.0002265195,0.0001765988],"domain_scores_gemma":[0.9994027,0.0002732289,0.00006105443,0.0002181909,0.00001889105,0.00002596193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007494171,0.0005954454,0.03302985,0.00007511917,0.0001499918,0.0001886804,0.08432916,0.02804903,0.08626665,0.6863763,0.003313862,0.07687645],"study_design_scores_gemma":[0.00262445,0.00396881,0.03718224,0.0001270195,0.00003014852,0.0002747387,0.2509842,0.6713965,0.0183672,0.009954329,0.003914446,0.001175885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4226795,0.00004049845,0.5707417,0.0006217178,0.0001199862,0.0002536116,0.000001927289,0.00001543185,0.005525608],"genre_scores_gemma":[0.9990193,0.000002502732,0.0003504804,0.0004583769,0.00001121184,0.00003181986,0.000001217553,0.000004209746,0.0001208984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.676422,"threshold_uncertainty_score":0.3413259,"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."}}