{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005839159,0.0006974759,0.0004272595,0.0007402284,0.0002462603,0.0006946784,0.0009721381,0.0004056941,0.00621445],"category_scores_gemma":[0.00274311,0.0003351108,0.0006214775,0.0006602725,0.0002873829,0.001200745,0.001234165,0.0005489986,0.001119521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000198585,"about_ca_system_score_gemma":0.0002637118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001210858,"about_ca_topic_score_gemma":0.001567456,"domain_scores_codex":[0.9993237,0.00009005555,0.00004260374,0.0001203168,0.0003529225,0.00007026936],"domain_scores_gemma":[0.9984758,0.0007290569,0.0001148758,0.0002507397,0.0003705498,0.00005891248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002375591,0.000189351,0.001004495,0.0003192475,0.00002735646,0.0002903788,0.0005349945,0.006677643,0.6066045,0.002475396,0.001486927,0.3801521],"study_design_scores_gemma":[0.0002295295,0.002010743,0.01241798,0.0001060148,0.0001848385,0.002346589,0.0003895104,0.2340614,0.7082987,0.002177962,0.03757233,0.000204429],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1470669,0.0008309433,0.840229,0.00008790378,0.0001041204,0.0002471502,0.0001014713,0.004673058,0.006659494],"genre_scores_gemma":[0.3713643,0.000661599,0.6221562,0.0001071546,0.00004116972,0.0001209181,0.0002110865,0.0004514143,0.004886159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00621445,"threshold_uncertainty_score":0.02078938,"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."}}