{"id":"W2013303975","doi":"10.1016/s0166-3615(00)00087-7","title":"A multi-sensor approach to automating co-ordinate measuring machine-based reverse engineering","year":2001,"lang":"en","type":"article","venue":"Computers in Industry","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Toronto Metropolitan University","funders":"","keywords":"Reverse engineering; Coordinate-measuring machine; Process (computing); Artificial intelligence; CAD; Computer science; Computer vision; Engineering drawing; Object (grammar); Structured light; Engineering; Mechanical engineering","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":[0.0007966787,0.0009436224,0.0008386544,0.001125843,0.0007742171,0.001242558,0.001526604,0.001374326,0.002655594],"category_scores_gemma":[0.001527446,0.0006768539,0.0007065386,0.0008513478,0.0005992679,0.001652568,0.001124605,0.00112722,0.001030568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006379915,"about_ca_system_score_gemma":0.0009141506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00181514,"about_ca_topic_score_gemma":0.004123379,"domain_scores_codex":[0.998376,0.0002300432,0.0000646247,0.0003382255,0.0009064219,0.00008463818],"domain_scores_gemma":[0.998982,0.0002079152,0.00009126036,0.0002587921,0.0004182447,0.00004176124],"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":[0.0002807509,0.0003477908,0.001397825,0.0002013696,0.0001039419,0.0002342527,0.0001903836,0.03068956,0.4557914,0.01417761,0.00220981,0.4943753],"study_design_scores_gemma":[0.00002710787,0.0003931521,0.001352576,0.00002311461,0.00006605723,0.0006271018,0.0000767353,0.685468,0.2927349,0.005731465,0.01341509,0.0000847131],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004263838,0.0001047251,0.9933152,0.00005418776,0.00005077936,0.00004673866,0.00002071406,0.0009483201,0.001195604],"genre_scores_gemma":[0.1294924,0.0001102654,0.8676672,0.0001023733,0.00002466633,0.00006873086,0.00004520355,0.00006200605,0.00242722],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002655594,"threshold_uncertainty_score":0.008883834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07989917297549166,"score_gpt":0.2821283821100538,"score_spread":0.2022292091345622,"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."}}