{"id":"W4207015432","doi":"10.22215/etd/2021-14721","title":"Improving VR Selection using Progressive Refinement with Multi-Touch Marking Menus","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Selection (genetic algorithm); Computer science; Object (grammar); Computer graphics (images); Artificial intelligence; Human–computer interaction","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"],"consensus_categories":[],"category_scores_codex":[0.00008830796,0.0003435194,0.0002845977,0.0002164217,0.0002922947,0.0003660676,0.0003752635,0.0001529173,0.000130545],"category_scores_gemma":[0.00002996898,0.0002911298,0.0001039908,0.0004480167,0.00001146666,0.0006105977,0.00009026249,0.0003700992,0.00001156374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002960763,"about_ca_system_score_gemma":0.0004018612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005871413,"about_ca_topic_score_gemma":0.0005831139,"domain_scores_codex":[0.9981,0.00006897428,0.000281962,0.0007727029,0.0003917463,0.0003846272],"domain_scores_gemma":[0.998371,0.00003085413,0.0004443423,0.0002997676,0.0007917575,0.00006225648],"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":[0.0003225999,0.0006185655,0.0007039673,0.0007248233,0.0009005473,0.0004706613,0.008355446,0.0002298599,0.8892381,0.002769975,0.0005113184,0.09515411],"study_design_scores_gemma":[0.001162101,0.0004971832,0.003674045,0.001548126,0.0002180026,0.0001731904,0.009040376,0.1978886,0.7834705,0.00001555011,0.0009302869,0.001382041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1320514,0.0008490949,0.8392676,0.00006050843,0.002744593,0.001194063,0.000005940924,0.0001300883,0.02369678],"genre_scores_gemma":[0.6063549,0.0000344731,0.3588335,0.0005939489,0.0004940917,0.0002772075,0.0009269319,0.0001581569,0.03232682],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4804341,"threshold_uncertainty_score":0.9999541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01771810073344739,"score_gpt":0.2898633214939466,"score_spread":0.2721452207604992,"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."}}