{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001543365,0.00008331978,0.0001115544,0.0001051274,0.0001511643,0.00005542696,0.00004772853,0.0001295484,0.0000553061],"category_scores_gemma":[0.00002910899,0.00007354759,0.0000311313,0.00009466236,0.00005990986,0.000071783,8.019782e-7,0.0001364237,0.000002212946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004309214,"about_ca_system_score_gemma":0.00006311126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001374884,"about_ca_topic_score_gemma":0.0001450471,"domain_scores_codex":[0.9990832,0.00009574351,0.0002674551,0.0001117245,0.0003633836,0.0000784961],"domain_scores_gemma":[0.9995028,0.0001596872,0.00001439838,0.0001242874,0.0001276987,0.00007111052],"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":[0.0001085151,0.0003646572,0.00002498593,0.0009964261,0.000472422,0.000003014335,0.003823028,0.5104755,0.06535961,0.05313004,0.0007698482,0.364472],"study_design_scores_gemma":[0.000548812,0.0001698155,0.0001566774,0.0002163547,0.0001393357,0.000009886838,0.0001042471,0.9946913,0.002575136,0.0006289379,0.0006513792,0.0001080769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.30133,0.001649074,0.6948239,0.0009080473,0.0004618975,0.0004898728,0.00002195777,0.0001436107,0.0001717027],"genre_scores_gemma":[0.9986698,0.0004694249,0.0006863595,0.00002064679,0.00002618115,0.00003633287,0.0000102484,0.00001433574,0.0000666652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6973398,"threshold_uncertainty_score":0.2999184,"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."}}