{"id":"W2093311953","doi":"10.1115/1.1828461","title":"Optimal Calibration of Parallel Kinematic Machines","year":2005,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jacobian matrix and determinant; Workspace; Calibration; Machine tool; Kinematics; Computer science; Spurious relationship; Inverse; Inverse kinematics; Software; Stewart platform; Algorithm; Machining; Tripod (photography); Control theory (sociology); Mathematics; Artificial intelligence; Engineering; Applied mathematics; Geometry; Mechanical engineering; Robot","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004765283,0.0001073422,0.0002970247,0.00008081688,0.00001571376,0.00001579868,0.000164126,0.00008586924,0.0001298081],"category_scores_gemma":[0.00007830131,0.00008355867,0.00011369,0.00008577137,0.000007759855,0.0001754291,0.0000125475,0.0001684767,0.000004518713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002957591,"about_ca_system_score_gemma":0.00002303467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.975778e-7,"about_ca_topic_score_gemma":6.549926e-7,"domain_scores_codex":[0.9988899,0.0000509715,0.0006191706,0.00005468579,0.0002554865,0.0001297708],"domain_scores_gemma":[0.9994534,0.0001138411,0.000165605,0.0001072625,0.00006251533,0.00009732733],"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":[0.00003153964,0.00003902935,5.732852e-7,0.00003481502,0.00003556168,0.000007583346,0.0000318433,0.9659513,0.02212429,0.00896555,0.0004915091,0.0022864],"study_design_scores_gemma":[0.0004020539,0.0002086627,0.000006278684,0.00005479296,0.00003903508,0.00007974329,0.00001072926,0.9888933,0.00505603,0.005141462,0.00002444053,0.00008344747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003578638,0.0001449175,0.995694,0.0001696197,0.0002162732,0.00007917122,9.928921e-7,0.00002350283,0.00009291861],"genre_scores_gemma":[0.2062864,0.00006606467,0.793407,0.00003250293,0.0001594301,0.000001169192,5.098929e-7,0.00001797376,0.00002890524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2027078,"threshold_uncertainty_score":0.3407424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916857105150857,"score_gpt":0.2302840644255831,"score_spread":0.2111154933740745,"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."}}