{"id":"W2110627214","doi":"10.1109/iecon.2013.6699832","title":"A laser scanning based reverse engineering system for 3D model generation","year":2013,"lang":"en","type":"article","venue":"","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laser scanning; Computer science; Reverse engineering; Conveyor system; Bottle; Scanner; Noise (video); Sample (material); Laser; Artificial intelligence; Computer vision; Engineering; Image (mathematics); Mechanical engineering; Optics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005716436,0.0005213562,0.0005075979,0.001206771,0.0006043709,0.0006927004,0.001144121,0.001087754,0.01608217],"category_scores_gemma":[0.001175092,0.0005880413,0.0006207994,0.0009417981,0.0003248094,0.00114887,0.001067614,0.0008916648,0.005057015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003613442,"about_ca_system_score_gemma":0.0008391212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369582,"about_ca_topic_score_gemma":0.002348052,"domain_scores_codex":[0.9990309,0.00006510744,0.00004064384,0.0001401351,0.0006848307,0.00003842937],"domain_scores_gemma":[0.9991515,0.0001713421,0.00005624156,0.0003123984,0.0002751047,0.00003341182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001969376,0.0001193427,0.001263731,0.0002811357,0.00004489219,0.0003936727,0.0003607858,0.007117697,0.5412554,0.005492006,0.01380822,0.4296662],"study_design_scores_gemma":[0.0001092901,0.0005413966,0.004472441,0.00006233276,0.00009329517,0.00263062,0.0001295666,0.2429462,0.5401978,0.002073719,0.206478,0.0002653985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01416805,0.000139639,0.9655295,0.0001472004,0.0001277363,0.000232,0.0005038928,0.01235172,0.006800205],"genre_scores_gemma":[0.1061642,0.0002058916,0.8789034,0.0001960879,0.00006417788,0.000346175,0.00115693,0.0009578289,0.01200524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01608217,"threshold_uncertainty_score":0.05380023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02936475187626792,"score_gpt":0.1890118184257447,"score_spread":0.1596470665494767,"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."}}