{"id":"W3100119367","doi":"10.1115/detc2001/cie-21301","title":"Steepest-Directed Tool Paths of Sculptured Parts: The Most Efficient Local Scheme in 3-Axis CNC Machining and its Mathematical Proof","year":2001,"lang":"en","type":"article","venue":"","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Machining; Tool path; Path (computing); Numerical control; Scheme (mathematics); Computer science; Algorithm; Engineering drawing; Mechanical engineering; Mathematics; Engineering; Mathematical analysis","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":[0.0002115683,0.00017299,0.0003170056,0.00008457412,0.00003298373,0.0000135449,0.0001369919,0.00007686782,0.00009679751],"category_scores_gemma":[0.0001423802,0.0001128432,0.00004916723,0.0005415154,0.00006742506,0.0000488622,0.00006487886,0.0002134223,0.000005504482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004547868,"about_ca_system_score_gemma":0.000006814884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008488734,"about_ca_topic_score_gemma":0.0000159918,"domain_scores_codex":[0.9988942,0.00003745113,0.0004063071,0.0001907113,0.0002184339,0.0002529401],"domain_scores_gemma":[0.9994998,0.0001433678,0.00004035968,0.0002202792,0.00004329174,0.00005290874],"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":[0.0001657417,0.001234983,0.00537143,0.0009207443,0.0004316137,0.0001308183,0.003210489,0.7049609,0.06215661,0.02301924,0.0005222851,0.1978751],"study_design_scores_gemma":[0.0001981854,0.00003103558,0.0005528426,0.00007338459,0.00001625313,0.00001119939,0.0001352284,0.9806079,0.01723324,0.0007289816,0.0002465149,0.0001652609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6412296,0.0005964084,0.3539484,0.0001244649,0.0000154688,0.0004515081,0.00000377343,0.0005979085,0.003032454],"genre_scores_gemma":[0.986941,0.00003151555,0.01283161,0.00003733649,0.00001052935,0.00005609927,0.0000026676,0.00002400154,0.0000652052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3457114,"threshold_uncertainty_score":0.4601611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008239079567620862,"score_gpt":0.2330679771920687,"score_spread":0.2248288976244479,"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."}}