{"id":"W2775102562","doi":"10.1109/iros.2017.8206483","title":"Application of response surface methodology for performing kinematic calibration of a 3-PSS/S parallel kinematic mechanism","year":2017,"lang":"en","type":"article","venue":"","topic":"Robotic Mechanisms and Dynamics","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Kinematics; Workspace; Constraint (computer-aided design); Calibration; Kinematic diagram; Computer science; Control theory (sociology); Inverse kinematics; Mechanism (biology); Surface (topology); Parallel manipulator; Mathematical optimization; Algorithm; Mathematics; Robot; Artificial intelligence; Kinematic chain; Geometry; Classical mechanics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009591162,0.0006612865,0.0004138683,0.000591015,0.0001908736,0.0004373169,0.0005173346,0.0005827714,0.001988713],"category_scores_gemma":[0.001420733,0.0002854088,0.0005032118,0.0004879375,0.0003320647,0.0003701815,0.0004217962,0.0004253626,0.0005883906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000214092,"about_ca_system_score_gemma":0.0005041756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007493504,"about_ca_topic_score_gemma":0.0007274488,"domain_scores_codex":[0.9994861,0.0001351935,0.00002761242,0.0000708693,0.0002563158,0.00002389378],"domain_scores_gemma":[0.9995066,0.0001675901,0.000085682,0.00009963686,0.0001326513,0.000007907103],"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":[0.0001362131,0.0001270065,0.00209232,0.0004045211,0.0000824957,0.0002022777,0.0003395483,0.4691914,0.2166765,0.008964312,0.0006833993,0.3011],"study_design_scores_gemma":[0.00001260903,0.0002137367,0.001136739,0.00001722845,0.00001169141,0.0001215941,0.0000520927,0.9421246,0.05207753,0.001385336,0.00281911,0.00002772317],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02193676,0.00003933638,0.9762497,0.00002692476,0.000007877141,0.00003756899,0.00001913615,0.0006927371,0.0009900258],"genre_scores_gemma":[0.6057402,0.0001130121,0.3925556,0.00002000697,0.000005226432,0.0001481642,0.00007704423,0.00009804285,0.001242739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001988713,"threshold_uncertainty_score":0.006652892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0472252071984931,"score_gpt":0.2988005231082718,"score_spread":0.2515753159097787,"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."}}