{"id":"W2170935086","doi":"10.1109/robot.2007.363975","title":"Performance Issues in Collaborative Haptic Training","year":2007,"lang":"en","type":"article","venue":"Proceedings - IEEE International Conference on Robotics and Automation/Proceedings","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Haptic technology; Trainer; Computer science; Human–computer interaction; Robot; Virtual reality; Simulation; Task (project management); Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003843776,0.0007774,0.0006802574,0.0005135363,0.00108732,0.002301599,0.00133633,0.001531347,0.004771654],"category_scores_gemma":[0.01416617,0.0002711859,0.0003296904,0.0004256009,0.0013822,0.002269691,0.002853999,0.0008572482,0.0009013682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007298816,"about_ca_system_score_gemma":0.0005370992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001159614,"about_ca_topic_score_gemma":0.0004590794,"domain_scores_codex":[0.995279,0.001476708,0.0002274553,0.0005865476,0.001850888,0.0005794737],"domain_scores_gemma":[0.9892504,0.007154998,0.0009469115,0.0008835618,0.001393483,0.0003706322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001592387,0.000450912,0.003544096,0.0008863477,0.0001021943,0.0008467148,0.001728621,0.4689721,0.07201044,0.1167054,0.002483369,0.3306773],"study_design_scores_gemma":[0.0000891769,0.001341457,0.00338748,0.0000768048,0.00004175585,0.0007816619,0.0004667407,0.9155185,0.02992324,0.04309157,0.005181821,0.00009983492],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.100137,0.00115085,0.8695009,0.0007792984,0.00009828034,0.0000587951,0.00003154966,0.0004814419,0.02776201],"genre_scores_gemma":[0.9811471,0.0001824955,0.01549828,0.00004552872,0.00007032842,0.00004367641,0.00002444724,0.00004568848,0.00294252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004771654,"threshold_uncertainty_score":0.02032804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03714001102228806,"score_gpt":0.2810048740458612,"score_spread":0.2438648630235731,"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."}}