{"id":"W2047445266","doi":"10.1115/1.1468864","title":"Generic Simulation Approach for Multi-Axis Machining, Part 2: Model Calibration and Feed Rate Scheduling","year":2002,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Machining; Deflection (physics); Scheduling (production processes); Mechanical engineering; Calibration; Computer science; Machine tool; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003985038,0.000123049,0.0001425603,0.0001941855,0.0001473328,0.0001222699,0.00008584682,0.00004056339,0.000001107185],"category_scores_gemma":[0.0001106228,0.0001133609,0.00002276524,0.0001129776,0.00002945995,0.0009094402,0.00001853683,0.0001311217,5.502184e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004216471,"about_ca_system_score_gemma":0.000008092432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.401581e-7,"about_ca_topic_score_gemma":1.046712e-7,"domain_scores_codex":[0.9992785,0.000003220093,0.0002419282,0.0001326709,0.0001484543,0.0001951716],"domain_scores_gemma":[0.9996694,0.00003716576,0.00007843949,0.00005791768,0.00006191492,0.00009519189],"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":[0.000003163727,0.000005323603,0.0000162026,0.0001052704,0.000006429325,3.715879e-7,0.0003021751,0.9908527,0.002524749,0.00001379699,0.000002732781,0.006167104],"study_design_scores_gemma":[0.0003171294,0.00002416915,0.0001829611,0.00003869801,0.00001349473,0.00001247268,0.0000317178,0.9922363,0.006947469,0.00001667936,0.00004750036,0.0001314263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.257691,0.0004281451,0.7416523,0.000009013633,0.0001008826,0.00006001748,9.542977e-7,0.00004243258,0.00001528894],"genre_scores_gemma":[0.7642489,0.0002271825,0.2354169,0.00001251829,0.00006609259,0.000002782184,7.618843e-7,0.00001611651,0.000008693325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5065579,"threshold_uncertainty_score":0.4622725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03481570658326264,"score_gpt":0.2441477072061018,"score_spread":0.2093320006228391,"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."}}