{"id":"W1993073331","doi":"10.1115/imece2004-62015","title":"Cutter Engagement Feature Extraction From Solid Models for End Milling","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Machining; Mechanical engineering; Computer science; Solid modeling; Process (computing); Rotation (mathematics); Engineering drawing; Geometry; Algorithm; Engineering; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003541256,0.001003689,0.0008577798,0.001285809,0.0003472443,0.001526625,0.001034783,0.001082734,0.002405439],"category_scores_gemma":[0.001414791,0.0007349993,0.001270748,0.0009193715,0.0003370779,0.0008699039,0.0009130991,0.001078811,0.001388164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004334559,"about_ca_system_score_gemma":0.00069723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001897491,"about_ca_topic_score_gemma":0.003749757,"domain_scores_codex":[0.9995802,0.00003638171,0.00002403457,0.00004836447,0.0002796065,0.00003140756],"domain_scores_gemma":[0.9993678,0.0002920782,0.00008737912,0.00009916585,0.0001290264,0.00002462076],"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":[0.0001657366,0.0001248809,0.002656179,0.0002750957,0.00004290694,0.0004459235,0.0001974221,0.5506587,0.07647511,0.009664209,0.003191825,0.356102],"study_design_scores_gemma":[0.000004908449,0.00003142665,0.0004480539,0.0000146682,0.000005029299,0.0001006632,0.00002254063,0.9846971,0.009862015,0.001817562,0.002982998,0.00001298649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01999988,0.0001290439,0.976434,0.00004012553,0.00001031304,0.00007015224,0.0002809477,0.001780945,0.001254533],"genre_scores_gemma":[0.3448291,0.0004076912,0.6488638,0.00002599251,0.00001510953,0.0001981446,0.002412962,0.0005461742,0.002701052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002405439,"threshold_uncertainty_score":0.008046985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01881294916981476,"score_gpt":0.2687824770385457,"score_spread":0.249969527868731,"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."}}