{"id":"W1978570739","doi":"10.1243/0954405021520391","title":"Tool path error prediction of a five-axis machine tool with geometric errors","year":2002,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine tool; Position (finance); CAD; Machining; Kinematics; Computer science; Computer Aided Design; Path (computing); Process (computing); Coordinate-measuring machine; Orientation (vector space); Geometric modeling; Engineering drawing; Algorithm; Engineering; Mathematics; Geometry; 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":[0.0004703257,0.0002873038,0.0005745798,0.0005152901,0.0000324082,0.000006798325,0.0003942888,0.000212386,0.00003506428],"category_scores_gemma":[0.0004126035,0.0002030715,0.0002382692,0.0006415776,0.00006751806,0.0003072015,0.00003790562,0.0006047583,4.499557e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134292,"about_ca_system_score_gemma":0.00001678526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001430008,"about_ca_topic_score_gemma":2.201559e-7,"domain_scores_codex":[0.9981588,0.000004433302,0.0008477293,0.0001391956,0.0005940778,0.0002557417],"domain_scores_gemma":[0.9989187,0.00004513241,0.0004513142,0.0001451005,0.0003697399,0.00007002895],"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":[0.0001549131,0.0001634766,0.000317626,0.001036188,0.0004714649,0.000003786772,0.0001774698,0.8352106,0.1575838,0.002336395,0.001343144,0.001201097],"study_design_scores_gemma":[0.001880768,0.001011699,0.001670417,0.00126512,0.0003918473,0.0001579082,0.00006022007,0.1041993,0.8845081,0.0002140448,0.004241284,0.0003992298],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8630629,0.001065472,0.1342072,0.0001037346,0.000800081,0.00038292,0.00004667126,0.0001675145,0.0001635852],"genre_scores_gemma":[0.9850548,0.0001852542,0.0145902,0.000009122496,0.00009313347,0.000008842959,0.000001379384,0.00003321567,0.00002403873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7310113,"threshold_uncertainty_score":0.8281015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302803316556306,"score_gpt":0.193338699703693,"score_spread":0.1803106665381299,"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."}}