{"id":"W2069337792","doi":"10.1007/s00170-012-4479-3","title":"Modeling of burr thickness in milling of ductile materials","year":2012,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Enhanced Data Rates for GSM Evolution; Materials science; Mechanical engineering; Scanning electron microscope; Sensitivity (control systems); Work (physics); Machining; Yield (engineering); Characterization (materials science); Process (computing); Engineering drawing; Composite material; Engineering; Computer science; Nanotechnology","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":[0.0002540378,0.00009312038,0.0002200466,0.0003500402,0.00001401474,0.000004791127,0.0004504308,0.00007103532,0.00001405599],"category_scores_gemma":[0.00008740689,0.00007284312,0.00003467208,0.00008882042,0.00004269561,0.0002916906,0.00006688266,0.0002081598,4.230604e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005399433,"about_ca_system_score_gemma":0.00001140573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005403572,"about_ca_topic_score_gemma":0.000002296081,"domain_scores_codex":[0.999095,0.000008596307,0.0005227226,0.0000517198,0.00017895,0.0001430409],"domain_scores_gemma":[0.9994325,0.00004819941,0.0002532535,0.0001101014,0.000140091,0.00001582518],"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":[0.00004773405,0.00001842328,0.00005527952,0.00003746548,0.00002942463,0.000001515496,0.0002126018,0.8890209,0.1049314,0.0008709906,9.077442e-7,0.004773324],"study_design_scores_gemma":[0.0003593832,0.00002873753,0.00006322153,0.0001904511,0.00001115739,0.00008292444,0.0003814522,0.01323507,0.9785869,0.006923118,0.00006225888,0.00007533086],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8779713,0.0008647968,0.1200999,0.0001387069,0.0007931885,0.00004402374,0.00000332101,0.00002875689,0.00005608022],"genre_scores_gemma":[0.978874,0.0007600878,0.02025157,0.000007457732,0.00008335659,0.000002325222,0.000001222549,0.00001698142,0.000003013944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8757858,"threshold_uncertainty_score":0.2970456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008545733691823909,"score_gpt":0.2442486439100884,"score_spread":0.2357029102182644,"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."}}