{"id":"W2886215671","doi":"10.1007/s12541-018-0132-x","title":"Experimental Investigation of Dynamic Errors in Coordinate Measuring Machines for High Speed Measurement","year":2018,"lang":"en","type":"article","venue":"International Journal of Precision Engineering and Manufacturing","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Compensation (psychology); Coordinate-measuring machine; Observational error; Computer science; Reduction (mathematics); Machine tool; Control theory (sociology); Simulation; Control engineering; Engineering; Mechanical engineering; Artificial intelligence; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005891668,0.000129522,0.000203165,0.0004614549,0.00001615271,0.00001376952,0.0001637595,0.00005376333,0.000005316586],"category_scores_gemma":[0.00009259225,0.0001229228,0.00005067285,0.00003763769,0.00002691015,0.0002229131,0.00002777067,0.0001217589,2.107923e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805278,"about_ca_system_score_gemma":0.00001011526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005237699,"about_ca_topic_score_gemma":0.000004913737,"domain_scores_codex":[0.9989878,0.00001077175,0.0004382427,0.00009579096,0.0003426292,0.0001247494],"domain_scores_gemma":[0.9995129,0.00004609386,0.0001243127,0.0000613356,0.0002073662,0.00004795123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001367842,0.00001874258,0.0003462325,0.00004662223,0.0001048972,0.000004495074,0.0002924163,0.03379054,0.9493914,0.00006242061,0.00002390222,0.01578155],"study_design_scores_gemma":[0.0008670585,0.0001348151,0.009118353,0.0003306816,0.00001159685,0.00002013143,0.00003005678,0.0220113,0.9664509,0.0007748983,0.0001370746,0.0001131772],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9013306,0.0004512332,0.09705104,0.0000326763,0.0009586293,0.0001062204,0.000002607612,0.00004042025,0.00002656525],"genre_scores_gemma":[0.9843277,0.00004584213,0.01547166,0.00000600658,0.0001181553,0.000004711051,0.000001406664,0.00002057124,0.000003960165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08299708,"threshold_uncertainty_score":0.5012645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114892087442649,"score_gpt":0.261283186584917,"score_spread":0.2401342657104905,"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."}}