{"id":"W897097795","doi":"10.1007/s00170-015-7532-1","title":"The edge–torus tangency problem in multipoint machining of triangulated surface models","year":2015,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Torus; Tangent; Surface (topology); Enhanced Data Rates for GSM Evolution; Tensor product; Geometry; Triangulation; Pyramid (geometry); Toroid; Mathematics; Computer science; Pure mathematics; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005265386,0.0001605206,0.0003047902,0.0003003272,0.00003498032,0.0000167242,0.001223567,0.00009292637,0.000003425162],"category_scores_gemma":[0.0001683579,0.00009988424,0.00009845503,0.0002293616,0.000129983,0.0002542631,0.0001661391,0.0005649498,0.000001474852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002816166,"about_ca_system_score_gemma":0.00003002398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001543524,"about_ca_topic_score_gemma":0.00002560362,"domain_scores_codex":[0.9984927,0.00003476383,0.000751408,0.0001115116,0.0003876832,0.0002219939],"domain_scores_gemma":[0.9988706,0.0001748225,0.0004038402,0.0002564282,0.0002506416,0.00004366107],"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":[0.0001400688,0.0000288122,0.00006640302,0.00000480634,0.0001413829,0.00002616735,0.0001744133,0.919035,0.01817138,0.0009998878,0.00007459817,0.06113714],"study_design_scores_gemma":[0.001235646,0.0001398713,0.00009159421,0.0001418345,0.00002629586,0.0001429566,0.0005556068,0.05004274,0.7214458,0.2232823,0.002696437,0.000198964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9160922,0.002273103,0.0782997,0.002015349,0.0004467767,0.0002120013,0.000003824182,0.0002103677,0.0004466336],"genre_scores_gemma":[0.9568293,0.0006471397,0.04240084,0.00001576833,0.00003788689,0.000007450228,8.54833e-7,0.00002492402,0.0000358291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8689922,"threshold_uncertainty_score":0.4073161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398483644113618,"score_gpt":0.2570967278077283,"score_spread":0.2431118913665921,"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."}}