{"id":"W2121344123","doi":"10.1109/3516.951359","title":"Optimal kinematic design of a haptic pen","year":2001,"lang":"en","type":"article","venue":"IEEE/ASME Transactions on Mechatronics","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Workspace; Pantograph; Haptic technology; Robot; Kinematics; Computer science; Actuator; Mechanism (biology); Optimal design; Simulation; Range (aeronautics); Mechanism design; Interface (matter); Control engineering; Engineering; Engineering drawing; Artificial intelligence; Mathematics","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":[0.0008746039,0.0007430352,0.0007391382,0.0004906529,0.0002975857,0.001014349,0.0006782399,0.0009752138,0.003447863],"category_scores_gemma":[0.001599064,0.0006757723,0.0003243208,0.0002961019,0.0005058658,0.0007659058,0.0007210994,0.0003618449,0.0007147429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003048838,"about_ca_system_score_gemma":0.0006035509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004460559,"about_ca_topic_score_gemma":0.000332072,"domain_scores_codex":[0.9995244,0.0001260959,0.00003928055,0.00008591591,0.0001876973,0.00003664525],"domain_scores_gemma":[0.999586,0.0001566694,0.00009172531,0.00005024038,0.00008875658,0.00002650875],"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":[0.0003802993,0.0001007523,0.0006038275,0.0007013187,0.00004023764,0.0004175777,0.0001722887,0.7562872,0.06433631,0.03780929,0.0008124891,0.1383384],"study_design_scores_gemma":[0.0001209339,0.0005049577,0.0002512628,0.00003386214,0.00002536743,0.0001499634,0.00003506656,0.9802114,0.00956757,0.004378593,0.004696115,0.00002486957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01431675,0.0001475892,0.9817538,0.0000407406,0.0000203269,0.00007928033,0.00001922086,0.0001479884,0.003474329],"genre_scores_gemma":[0.5778495,0.0004192719,0.4168938,0.00003413473,0.00002097215,0.0003025508,0.00003877755,0.00005829522,0.004382844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003447863,"threshold_uncertainty_score":0.01153421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02292562974676167,"score_gpt":0.2269845641442634,"score_spread":0.2040589343975018,"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."}}