{"id":"W4399126753","doi":"10.1109/vrw62533.2024.00408","title":"Target Selection with Avatars in Mixed Reality","year":2024,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Avatar; Selection (genetic algorithm); Computer science; Virtual reality; Mixed reality; Fitts's law; Human–computer interaction; Artificial intelligence; Engineering","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":[0.002410912,0.0008605131,0.0008524298,0.0003337564,0.0003883585,0.001617648,0.0009393123,0.001030044,0.00610884],"category_scores_gemma":[0.01635376,0.0004861201,0.0004358206,0.0002025676,0.000571633,0.00177157,0.00173942,0.0007040171,0.001257058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001815981,"about_ca_system_score_gemma":0.0001666108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004112229,"about_ca_topic_score_gemma":0.0003365783,"domain_scores_codex":[0.9974057,0.001472345,0.0001410163,0.0003357667,0.0004826946,0.0001624263],"domain_scores_gemma":[0.9883583,0.009507631,0.0004507123,0.0008648701,0.0004505393,0.0003679935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.02725607,0.006090372,0.01013674,0.001580164,0.0003192762,0.001276158,0.01055522,0.01721893,0.6728296,0.00837268,0.002420985,0.2419438],"study_design_scores_gemma":[0.005316185,0.1119744,0.06581961,0.0006471925,0.001034439,0.006737662,0.007065351,0.3392211,0.4057873,0.01955132,0.03620925,0.0006362944],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9414259,0.0001507381,0.05142886,0.0001030153,0.00007043318,0.0001844812,0.00007449421,0.0004858355,0.006076285],"genre_scores_gemma":[0.9538755,0.0001074346,0.04087018,0.000145148,0.00002440385,0.0002053546,0.0001095904,0.00014028,0.004522071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00610884,"threshold_uncertainty_score":0.02043617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01527020868360902,"score_gpt":0.2612139998385374,"score_spread":0.2459437911549284,"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."}}