{"id":"W1971712760","doi":"10.1007/s12008-015-0262-7","title":"Idealization of scanning-derived triangle mesh models of prismatic engineering parts","year":2015,"lang":"en","type":"article","venue":"International Journal on Interactive Design and Manufacturing (IJIDeM)","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"CAD; Computer Aided Design; Reverse engineering; Parametric statistics; Feature (linguistics); Workflow; Engineering drawing; Mesh generation; Software; Triangle mesh; Computer science; Parametric model; Triangulation; Engineering; Finite element method; Structural engineering; Mechanical engineering; Polygon mesh; Geometry; Mathematics; Computer graphics (images)","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":[0.0003923356,0.0002082143,0.0003217369,0.0004221256,0.00002981518,0.00007691458,0.0002255984,0.00007664459,0.00003099163],"category_scores_gemma":[0.0001275643,0.0001926788,0.00008421752,0.00004779852,0.00002476409,0.0005892701,0.0000394221,0.0002406142,0.000002117861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001443049,"about_ca_system_score_gemma":0.00003551222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008394801,"about_ca_topic_score_gemma":6.732805e-7,"domain_scores_codex":[0.9986113,0.00005642235,0.000592596,0.0001494453,0.0004247571,0.0001654264],"domain_scores_gemma":[0.9989592,0.0002037824,0.0003353712,0.00009961563,0.0002679828,0.0001339933],"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":[0.000208764,0.00003828069,0.00001529384,0.00005876974,0.0002253973,0.00001223362,0.001203298,0.9936785,0.001374281,0.0004575072,0.0002781547,0.002449533],"study_design_scores_gemma":[0.001163674,0.0001706385,0.0003274517,0.000557626,0.00003702602,0.0000753841,0.0001519144,0.521361,0.4723175,0.003209995,0.0004058497,0.0002219364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2508987,0.0001535703,0.7469999,0.00007758501,0.0008681213,0.0001774308,0.000007247962,0.00006448469,0.0007529],"genre_scores_gemma":[0.9932793,0.0001723033,0.006308557,0.00003493277,0.0001029893,0.00001084609,0.000008652966,0.00003601197,0.0000464411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7423805,"threshold_uncertainty_score":0.7857213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063699115591945,"score_gpt":0.2503820888361666,"score_spread":0.2197450976802471,"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."}}