{"id":"W2039988673","doi":"10.1109/3dui.2007.340783","title":"Exploring the Effects of Environment Density and Target Visibility on Object Selection in 3D Virtual Environments","year":2007,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"European Regional Development Fund; Vlaamse regering","keywords":"Visibility; Computer science; Cursor (databases); Selection (genetic algorithm); 3D interaction; Human–computer interaction; Artificial intelligence; Virtual machine; Computer vision; Virtual image; Set (abstract data type); Object (grammar); Virtual reality","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":[0.001588347,0.0009597794,0.000550332,0.000535355,0.0003464676,0.0008978691,0.0007867819,0.0005058248,0.0009609016],"category_scores_gemma":[0.01862036,0.0007295207,0.000319112,0.000349547,0.0005412834,0.001498164,0.001441217,0.0004593339,0.0001190386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002406748,"about_ca_system_score_gemma":0.0002697655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002357888,"about_ca_topic_score_gemma":0.002855205,"domain_scores_codex":[0.9983107,0.0008689314,0.00008065051,0.0001353313,0.0004460486,0.000158329],"domain_scores_gemma":[0.9660471,0.03133082,0.001085543,0.0005215788,0.0005918003,0.0004231544],"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":[0.003442782,0.001321537,0.0393243,0.001431687,0.000334433,0.001084924,0.006668744,0.07450266,0.5849989,0.001603717,0.0007464167,0.2845399],"study_design_scores_gemma":[0.0004726901,0.01131968,0.1973543,0.0001741504,0.0009741655,0.002706045,0.00283407,0.4493373,0.3295245,0.001625596,0.003290677,0.0003867064],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9643957,0.0002501406,0.03424739,0.00003394399,0.000007368775,0.0000362164,0.00001720601,0.0003424522,0.0006695796],"genre_scores_gemma":[0.9791489,0.0002036945,0.02031862,0.00001565792,0.000006098101,0.00002035018,0.00002742864,0.00006742316,0.0001917647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002357888,"threshold_uncertainty_score":0.008400083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417035373298597,"score_gpt":0.2227532337392765,"score_spread":0.2085828800062905,"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."}}