{"id":"W2604245549","doi":"10.1109/vr.2017.7892295","title":"Gauntlet: Travel technique for immersive environments using non-dominant hand","year":2017,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Viewport; Fist; Computer science; Human–computer interaction; Virtual reality; Gesture; Computer graphics (images); Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001269415,0.0001378197,0.0001531256,0.00005615184,0.0006865413,0.0001438635,0.0008459142,0.00005715744,0.00002892372],"category_scores_gemma":[0.00002925438,0.0001179879,0.0001131541,0.00001974538,0.0001013617,0.0007502937,0.0002371476,0.00007117954,0.00003696353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006394598,"about_ca_system_score_gemma":0.00003136966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001102961,"about_ca_topic_score_gemma":0.000002792002,"domain_scores_codex":[0.9991289,0.00001151643,0.0001306288,0.0003340595,0.0001119018,0.0002829839],"domain_scores_gemma":[0.9990982,0.00003773444,0.0001572043,0.000610227,0.00003795007,0.00005872986],"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":[0.00001576772,0.00003935345,0.00007459047,0.000004712051,0.00001987659,0.000006693264,0.0002522898,0.000002648176,0.9974953,0.001626976,0.0004458928,0.0000159278],"study_design_scores_gemma":[0.0004661906,0.0001085264,0.003628667,0.00002581757,0.00001030164,0.00001460807,0.0001271346,0.006968754,0.9870605,0.0003471751,0.001068835,0.0001734299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001573684,0.00000688366,0.993071,0.0002225719,0.0002815937,0.0004889147,0.000008064614,1.086919e-7,0.004347171],"genre_scores_gemma":[0.9914228,0.000003340234,0.006701471,0.0002898092,0.00004765148,0.00005268088,0.000002118871,0.00001014463,0.001469986],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9898491,"threshold_uncertainty_score":0.528039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02644503593129664,"score_gpt":0.2975332085455475,"score_spread":0.2710881726142509,"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."}}