{"id":"W1902809907","doi":"10.24908/pceea.v0i0.3966","title":"3D DIGITIZING TECHNOLOGY IN PRODUCT REVERSE DESIGN","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Digitization; Reverse engineering; Computer science; Laser scanning; Scanner; Product (mathematics); 3d scanning; Product design; Engineering drawing; 3d model; Computer vision; Artificial intelligence; Engineering; Laser; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.001448262,0.0007017871,0.0005029049,0.002043608,0.000607161,0.002316987,0.001035325,0.001226558,0.004625257],"category_scores_gemma":[0.002148469,0.0006474507,0.0005725016,0.002446065,0.002069625,0.002291669,0.001501618,0.001373828,0.002066824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001372639,"about_ca_system_score_gemma":0.0008267892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002014973,"about_ca_topic_score_gemma":0.00210899,"domain_scores_codex":[0.9977292,0.0004885428,0.00006699241,0.0001984859,0.001447017,0.00006977212],"domain_scores_gemma":[0.9988006,0.0004677256,0.0001243209,0.0003134784,0.0002701585,0.00002363416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008098368,0.00003899847,0.002144797,0.0009041826,0.00004699315,0.0004267241,0.0009531392,0.02425273,0.07422117,0.1505096,0.008941119,0.7374796],"study_design_scores_gemma":[0.00003314067,0.0003639226,0.005599423,0.0007050448,0.0001077676,0.004052839,0.0007278827,0.08943932,0.1728373,0.09127879,0.6345989,0.000255646],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01213666,0.01577153,0.9383909,0.00115459,0.0003535649,0.00009198931,0.000126152,0.0009741355,0.03100045],"genre_scores_gemma":[0.1817777,0.0229976,0.7736964,0.0009080573,0.0002463115,0.0001500837,0.0002403993,0.0002657147,0.01971779],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004625257,"threshold_uncertainty_score":0.01547307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01692404029468424,"score_gpt":0.1715497096371971,"score_spread":0.1546256693425129,"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."}}