{"id":"W4414543197","doi":"10.1007/s00170-025-16570-z","title":"Robust quasi-static errors predictive modeling for real-time error compensation in CNC machine tools","year":2025,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Compensation (psychology); Machine tool; Machining; Numerical control; Artificial neural network; Process (computing); Displacement (psychology); Model predictive control; Control theory (sociology)","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":[0.0006857665,0.0006854545,0.0008130911,0.0003106307,0.0004175786,0.0009593223,0.0009961976,0.0006924351,0.001522426],"category_scores_gemma":[0.001544868,0.0004297307,0.0004618695,0.000494566,0.000518364,0.000778439,0.0005360827,0.0009638127,0.0003243545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006312645,"about_ca_system_score_gemma":0.001005184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0138607,"about_ca_topic_score_gemma":0.01197948,"domain_scores_codex":[0.9995537,0.00007847755,0.00001830684,0.0001029459,0.0001952689,0.00005117708],"domain_scores_gemma":[0.9995229,0.0002065883,0.00007487035,0.00005636494,0.0001296232,0.000009712558],"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":[0.0001054674,0.0000327191,0.000190361,0.00009546345,0.0000278096,0.00004606869,0.00005217912,0.9586728,0.006053606,0.002703194,0.0005266945,0.03149367],"study_design_scores_gemma":[0.000001379169,0.00001014656,0.00007295969,0.000002434542,0.000002446442,0.000003215318,0.000001716423,0.9988475,0.0007114907,0.0002316985,0.0001129674,0.000002120568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02787584,0.000458847,0.968298,0.0001280143,0.0001019969,0.00002994605,0.00006484367,0.0006141328,0.002428401],"genre_scores_gemma":[0.9780512,0.000217932,0.01889813,0.00004002133,0.00002166953,0.00004264622,0.00006211797,0.00006035927,0.002605989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0138607,"threshold_uncertainty_score":0.02756006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02609096399238096,"score_gpt":0.2787033535423712,"score_spread":0.2526123895499903,"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."}}