{"id":"W4398155380","doi":"10.1016/j.mechatronics.2024.103205","title":"A self-tuning dual impedance control architecture for collaborative haptic training","year":2024,"lang":"en","type":"article","venue":"Mechatronics","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Institute for Medical Research Development; Iran National Science Foundation","keywords":"Haptic technology; Dual (grammatical number); Impedance control; Architecture; Training (meteorology); Computer science; Control (management); Human–computer interaction; Electrical impedance; Simulation; Control engineering; Engineering; Artificial intelligence; Electrical engineering; Physics","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":[0.0002349824,0.0001679935,0.000214262,0.00008363913,0.00008284742,0.0001579553,0.00007466875,0.00008840494,0.00001593258],"category_scores_gemma":[0.00002809854,0.0001575951,0.00007632465,0.0001816225,0.00001036855,0.00009380178,0.000006789297,0.000181297,0.00003074688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001153133,"about_ca_system_score_gemma":0.0001287865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001007885,"about_ca_topic_score_gemma":0.00003117985,"domain_scores_codex":[0.9991654,0.00002352035,0.0001887655,0.000190183,0.0001229754,0.0003091883],"domain_scores_gemma":[0.9995794,0.0001629372,0.00001613024,0.0001268306,0.00004819278,0.0000665569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006232982,0.00003288269,0.000009013548,0.00200444,0.001405134,0.00005139286,0.06668574,0.2822226,0.0435013,0.5112677,0.00845774,0.08429976],"study_design_scores_gemma":[0.0004694453,0.00006254098,6.929446e-7,0.00006440073,0.00003699649,0.00001739637,0.001152979,0.7600269,0.0002176854,0.0003888868,0.2374009,0.0001611792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008277994,0.009653871,0.9756184,0.0005775889,0.001672498,0.0008510233,0.0001344379,0.001688136,0.001526021],"genre_scores_gemma":[0.9859595,0.00004127289,0.01309524,0.00006286589,0.0003986492,0.0002031793,0.00001297603,0.00006244607,0.0001638343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9776816,"threshold_uncertainty_score":0.6426544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124254267627692,"score_gpt":0.2269087274036851,"score_spread":0.2144833006409159,"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."}}