{"id":"W4390576562","doi":"10.1109/tmrb.2024.3349612","title":"Evaluation of Communication and Human Response Latency for (Human) Teleoperation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Robotics and Bionics","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Rogers Corporation","keywords":"Teleoperation; Latency (audio); Computer science; Position tracking; Ethernet; Haptic technology; Augmented reality; Simulation; Real-time computing; Human–computer interaction; Robot; Artificial intelligence; Computer network; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009016989,0.0005061436,0.0003206888,0.0004095694,0.0001916424,0.0004599581,0.0002984378,0.0004403676,0.001889289],"category_scores_gemma":[0.005822156,0.0001063472,0.0001278805,0.0002707767,0.0002691318,0.0004891388,0.0004182116,0.000245152,0.0002460709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002549067,"about_ca_system_score_gemma":0.0002468285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054698,"about_ca_topic_score_gemma":0.0006920159,"domain_scores_codex":[0.9989284,0.0004080111,0.00008267091,0.0001149536,0.0003347958,0.0001312843],"domain_scores_gemma":[0.995682,0.002943623,0.0003381572,0.0002442229,0.0006350175,0.0001570018],"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":[0.009154943,0.001125771,0.04036923,0.00123049,0.0002590596,0.001541002,0.001693223,0.03104608,0.709364,0.0009245882,0.0009394158,0.2023522],"study_design_scores_gemma":[0.0004007712,0.0307851,0.2049786,0.00008244959,0.0005269374,0.004872164,0.001847444,0.2433287,0.5070774,0.0009008343,0.004974721,0.0002249688],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.959427,0.0003113195,0.03879444,0.00004978191,0.00003762756,0.00009970924,0.0001030778,0.0003268728,0.0008501344],"genre_scores_gemma":[0.9954022,0.00006631009,0.003992763,0.00001662294,0.000009708235,0.00004002312,0.00007904434,0.00002093416,0.0003722374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001889289,"threshold_uncertainty_score":0.006320298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03958999403450635,"score_gpt":0.3143272217693979,"score_spread":0.2747372277348916,"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."}}