{"id":"W3214572536","doi":"10.1115/imece2000-1909","title":"Generic Simulation Approach for Multi-Axis Machining: Part II—Model Calibration and Feed Rate Scheduling","year":2000,"lang":"en","type":"article","venue":"Manufacturing engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Machining; Computer science; Deflection (physics); Scheduling (production processes); Mechanical engineering; Calibration; Machine tool; Airfoil; Engineering; Structural engineering; Mathematical optimization; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001173785,0.0002495127,0.0001903709,0.00009884037,0.0001827988,0.00007575074,0.00007901224,0.0001005693,0.00001090756],"category_scores_gemma":[0.00002380034,0.0002734499,0.00004227414,0.00007459041,0.000008654232,0.0003776354,0.00002200179,0.0001525951,6.125678e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004600196,"about_ca_system_score_gemma":0.000005188934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002284192,"about_ca_topic_score_gemma":5.902434e-7,"domain_scores_codex":[0.9990858,0.000005679581,0.0002481617,0.0002811423,0.00008031823,0.0002988301],"domain_scores_gemma":[0.9996947,0.00003977603,0.00003036535,0.0001409809,0.00001406062,0.00008013736],"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":[0.00001053045,0.000009415709,0.000009082453,0.0002275414,0.00001836302,2.780087e-7,0.0002435333,0.9884355,0.0006198434,0.00002394274,0.000004022275,0.01039796],"study_design_scores_gemma":[0.0004785579,0.00001596807,0.0001032348,0.00003162071,0.00001971319,0.000001887941,0.000009231238,0.9904716,0.008078728,0.00002100642,0.0004535592,0.0003149171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2255009,0.0002042709,0.7733087,0.000005116498,0.00008254754,0.0002126602,0.000007884987,0.0005997571,0.00007811077],"genre_scores_gemma":[0.7094021,0.00006983095,0.290073,0.00001702002,0.00009295841,0.0000552809,0.00007946509,0.0000751316,0.0001351001],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4839012,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02349380313227566,"score_gpt":0.2327743212748505,"score_spread":0.2092805181425748,"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."}}