{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005281073,0.0006030883,0.0005838163,0.0009626234,0.0003345757,0.001540886,0.001523702,0.001281586,0.005497267],"category_scores_gemma":[0.002221559,0.0005066569,0.000919304,0.00100573,0.0007138647,0.000639897,0.0008163059,0.0006607088,0.0009893427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005294353,"about_ca_system_score_gemma":0.001240741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004255591,"about_ca_topic_score_gemma":0.004015723,"domain_scores_codex":[0.9993551,0.0000882621,0.00003897619,0.00006343726,0.0004031512,0.00005109007],"domain_scores_gemma":[0.9993753,0.0001875888,0.0000591627,0.0001462644,0.0002075732,0.00002420394],"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":[0.0001068134,0.00004377657,0.0008242757,0.0001375231,0.00002070618,0.0002760408,0.0001189417,0.9344073,0.006807234,0.02917881,0.0009310789,0.02714748],"study_design_scores_gemma":[0.000005136957,0.00001183698,0.0001307228,0.000008641167,0.00000406113,0.00007138005,0.00002060116,0.9945883,0.001340443,0.002686671,0.001125123,0.000007118919],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03123687,0.000116078,0.9528763,0.00009367736,0.00004283831,0.0001167213,0.0005283036,0.0006215749,0.01436773],"genre_scores_gemma":[0.7735432,0.0003148229,0.219913,0.00005599219,0.00001990968,0.0001863532,0.001384633,0.000333118,0.004248901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005497267,"threshold_uncertainty_score":0.01839024,"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."}}