{"id":"W2083628576","doi":"10.1145/2659766.2661221","title":"Depth cues and mouse-based 3D target selection","year":2014,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Computer science; Artificial intelligence; Cursor (databases); Selection (genetic algorithm); 3D interaction; Computer graphics (images); Tracking (education); Optical head-mounted display; Virtual reality; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00007364291,0.00006014901,0.00005706869,0.00004753153,0.00006923216,0.00006472326,0.0001253631,0.00002105967,0.00003956593],"category_scores_gemma":[0.00002102154,0.00004945913,0.00001902048,0.00007914258,0.0000128631,0.0003106208,0.00003228821,0.00004756016,0.00006656913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001182532,"about_ca_system_score_gemma":0.00001246626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004599143,"about_ca_topic_score_gemma":0.00002080148,"domain_scores_codex":[0.9995554,0.00003198274,0.00005590475,0.0001700867,0.00007009644,0.0001165375],"domain_scores_gemma":[0.9997408,0.00003797358,0.00002219118,0.0001009402,0.00006311978,0.00003491217],"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":[0.00005396279,0.0002518866,0.05369711,0.00003918317,0.00005939018,0.00000286385,0.0008479518,0.0005407033,0.681783,0.2077077,0.03346892,0.02154735],"study_design_scores_gemma":[0.0002771348,0.0001954681,0.0186977,0.000004659671,0.000002659679,0.000004026655,0.0000179474,0.3393392,0.6255057,0.0005145281,0.01529197,0.000148965],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03749368,0.000007056181,0.9341276,0.0003014518,0.00009049928,0.00003836794,2.400254e-7,0.00003373627,0.02790743],"genre_scores_gemma":[0.965472,8.605576e-7,0.03199935,0.001640701,0.00003132525,0.000003133904,0.000001422486,0.000003445829,0.0008477063],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9279784,"threshold_uncertainty_score":0.2016885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008012117702017471,"score_gpt":0.2317198665441804,"score_spread":0.2237077488421629,"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."}}