{"id":"W1970936586","doi":"10.1016/j.ijmachtools.2014.09.002","title":"A new approach to contour error control in high speed machining","year":2014,"lang":"en","type":"article","venue":"International Journal of Machine Tools and Manufacture","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Traverse; Machining; Interpolation (computer graphics); Machine tool; Controller (irrigation); Software; Process (computing); Computer science; Engineering; Numerical control; Table (database); Control engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004502993,0.000169255,0.000348751,0.000238004,0.00001872345,0.000171491,0.0002818029,0.00006719829,0.0000709403],"category_scores_gemma":[0.0001446015,0.0001341945,0.00006860344,0.00004112558,0.000007617745,0.0002225538,0.00002409556,0.0004239679,0.000008882114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007165198,"about_ca_system_score_gemma":0.00001567983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001047202,"about_ca_topic_score_gemma":0.00003297684,"domain_scores_codex":[0.9989204,0.00007912949,0.0004154108,0.0001203072,0.0003067471,0.0001579703],"domain_scores_gemma":[0.999433,0.0001360587,0.0001298201,0.00007819116,0.00008429465,0.0001385654],"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":[0.0008266752,0.00008808831,0.01209607,0.00005718107,0.0007801474,0.0001196192,0.003032011,0.7644151,0.01135096,0.003913304,0.01190284,0.1914179],"study_design_scores_gemma":[0.02315435,0.000504445,0.2432689,0.0005570627,0.000103233,0.001180269,0.0002148875,0.5374069,0.0009113876,0.002208814,0.1894588,0.00103096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4152278,0.0005243373,0.5686623,0.003439932,0.002041449,0.0003374284,0.00005208192,0.00006531547,0.00964933],"genre_scores_gemma":[0.9951231,0.000005601879,0.002956227,0.0006996806,0.0008128232,0.000001584081,0.000008016635,0.00002217001,0.0003707443],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5798954,"threshold_uncertainty_score":0.5472295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008235111171242973,"score_gpt":0.2339533581505335,"score_spread":0.2257182469792905,"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."}}