{"id":"W2282500447","doi":"10.1139/tcsme-2004-0004","title":"MULTI-POINT MACHINING WITH ANTI-GOUGING AND SCALLOP HEIGHT CONTROL","year":2004,"lang":"en","type":"article","venue":"Transactions of the Canadian Society for Mechanical Engineering","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Machining; Point (geometry); Scallop; Position (finance); Estimator; Mechanical engineering; Multi point; Interference (communication); Computer science; Engineering; Geometry; Mathematics; Applied mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0001053528,0.0001728727,0.0002537266,0.00005436403,0.0001781819,0.00001826313,0.0001586393,0.000101792,0.000005327084],"category_scores_gemma":[0.00001342944,0.0001404419,0.0002830513,0.0002720506,0.00004535961,0.0001178371,0.000003702378,0.0002763044,2.540274e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002863864,"about_ca_system_score_gemma":0.00004527753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001736074,"about_ca_topic_score_gemma":0.005502019,"domain_scores_codex":[0.9992402,0.000003612822,0.0002006531,0.0001393017,0.00009736538,0.0003188805],"domain_scores_gemma":[0.9995038,0.00004865973,0.00002526011,0.0001884677,0.00003432234,0.0001994953],"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":[0.000002953052,0.0000111255,0.000007190121,0.00009333168,0.0002661315,5.815712e-7,0.0001745369,0.9653501,0.03187412,0.0008851594,0.000004957349,0.001329822],"study_design_scores_gemma":[0.001251775,0.00005935535,0.0000922999,0.00015576,0.0002258982,0.00001985162,0.0001138707,0.95276,0.04420238,0.0003342411,0.0004322688,0.0003523442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008520169,0.000129861,0.9903933,0.0003494695,0.00006237694,0.0002731222,0.00004474909,0.0002190424,0.000007949631],"genre_scores_gemma":[0.8301997,0.00002138588,0.1696028,0.00006853967,0.00001323552,0.00004393405,0.000001430199,0.00004293487,0.000006015505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8216796,"threshold_uncertainty_score":0.5727055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005013129896021592,"score_gpt":0.1890995244084078,"score_spread":0.1840863945123862,"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."}}