{"id":"W2034203039","doi":"10.1109/haptics.2008.4479948","title":"Perceptual Rendering for Learning Haptic Skills","year":2008,"lang":"en","type":"article","venue":"","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Rendering (computer graphics); Computer science; Haptic technology; Perception; High fidelity; Human–computer interaction; Fidelity; Collision detection; Salient; Artificial intelligence; Simulation; Collision; Computer vision; Psychology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004744722,0.00007178939,0.00009257369,0.00003655839,0.00009812987,0.00001575547,0.00004130686,0.00003740445,0.0003071861],"category_scores_gemma":[0.00002523993,0.00006770239,0.00003831289,0.00003806183,0.00001138799,0.00006525392,0.000005172743,0.00005831926,0.0001446261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002349076,"about_ca_system_score_gemma":0.000006125689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005662433,"about_ca_topic_score_gemma":0.00000790102,"domain_scores_codex":[0.9995846,0.000005455051,0.0001250532,0.00007993686,0.00006452725,0.0001404538],"domain_scores_gemma":[0.9998282,0.00003536406,0.000006330489,0.00006803354,0.00001880134,0.00004333234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002007977,0.0001360378,0.0132624,0.0004633196,0.0003438322,0.00004185732,0.04879019,0.6396999,0.1552928,0.01985881,0.07101071,0.05108],"study_design_scores_gemma":[0.001006868,0.0000992841,0.005551297,0.00003051943,0.00001162241,0.0001756295,0.0023588,0.8274457,0.002950078,0.00002142865,0.1598392,0.000509497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.568056,0.0001261711,0.3605486,0.000042773,0.0006023649,0.0002602035,0.000001368182,0.001379875,0.06898265],"genre_scores_gemma":[0.9865527,0.00002011464,0.003702124,0.00003003508,0.0001465589,0.00002286371,0.000003885361,0.00002269526,0.009499],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4184967,"threshold_uncertainty_score":0.3363472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01811490194812903,"score_gpt":0.2088151122164535,"score_spread":0.1907002102683244,"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."}}