{"id":"W2064343291","doi":"10.1007/s00170-007-1062-4","title":"A reverse engineering methodology for rotary components from point cloud data","year":2007,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reverse engineering; Point cloud; Representation (politics); Point (geometry); Component (thermodynamics); Process (computing); Engineering design process; Computer science; Engineering; Cloud computing; Engineering drawing; Systems engineering; Mechanical engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007323105,0.0001686006,0.000253346,0.0003676922,0.00004563977,0.00002521774,0.00171149,0.0001268105,0.00002453341],"category_scores_gemma":[0.0002603256,0.0001356179,0.00006607266,0.00006144116,0.00004173065,0.0002839135,0.0002683711,0.0003948868,0.000002719104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001223299,"about_ca_system_score_gemma":0.00001305788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001144605,"about_ca_topic_score_gemma":0.00001295444,"domain_scores_codex":[0.9988273,0.00001074641,0.0005230271,0.0001699901,0.0002245542,0.000244371],"domain_scores_gemma":[0.998735,0.0004591486,0.0002415061,0.000396099,0.0001233979,0.00004480945],"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":[0.0005443352,0.00004820374,0.00006641803,0.00005229192,0.0007513888,0.00009421402,0.0002311333,0.8463581,0.05332347,0.0009132831,0.001246421,0.09637076],"study_design_scores_gemma":[0.001498496,0.00007681882,0.0006913339,0.000143338,0.00006941516,0.0002443699,0.0002206492,0.02440985,0.9038255,0.01654967,0.0519911,0.0002794724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4403005,0.0003824828,0.5552863,0.001190391,0.002497621,0.0001270504,0.00003595879,0.000150961,0.00002870272],"genre_scores_gemma":[0.7321294,0.000287288,0.2668247,0.0001201579,0.0005367668,0.000004705327,0.0000457112,0.00003779234,0.00001349761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.850502,"threshold_uncertainty_score":0.5530337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03699500516919584,"score_gpt":0.2827025977971852,"score_spread":0.2457075926279894,"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."}}