{"id":"W2155206557","doi":"10.1109/cimat.1994.389057","title":"Tolerance analysis in setup and fixture planning for precision machining","year":2002,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Fixture; Geodetic datum; Machining; Computer science; Tolerance analysis; Transformation (genetics); Process (computing); Selection (genetic algorithm); Computer-aided; Engineering drawing; Algorithm; Industrial engineering; Engineering; Artificial intelligence; Mechanical engineering; Programming language","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":[0.0009305102,0.0007403162,0.0007603528,0.001035206,0.0004971896,0.0008125865,0.0006403521,0.0004279389,0.002012212],"category_scores_gemma":[0.00254616,0.0004859427,0.0004373507,0.001350237,0.0006424485,0.0009596577,0.0008387695,0.0005997893,0.0003244226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000521272,"about_ca_system_score_gemma":0.0009527439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003866745,"about_ca_topic_score_gemma":0.003289379,"domain_scores_codex":[0.9988092,0.0002631122,0.00004587802,0.0001322362,0.0006939916,0.00005553539],"domain_scores_gemma":[0.9994764,0.0002601948,0.00007686327,0.00005892609,0.000115001,0.00001266189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004168272,0.00001321673,0.0003936806,0.000105328,0.00001722728,0.00004662548,0.00006902638,0.800102,0.004713554,0.01883276,0.0009545498,0.1747103],"study_design_scores_gemma":[0.000009689441,0.00007955095,0.0006374022,0.00003112439,0.00002216309,0.00009242014,0.00002839708,0.9659822,0.007056985,0.02099602,0.005040007,0.00002404217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004224811,0.000287049,0.9940891,0.00002488751,0.00001271984,0.00001259378,0.00001679859,0.000171437,0.001160629],"genre_scores_gemma":[0.4222257,0.001405528,0.5710503,0.00004044867,0.00005488552,0.0001287164,0.0002524065,0.0003496649,0.004492504],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003866745,"threshold_uncertainty_score":0.007688463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01256305769009348,"score_gpt":0.222187831842736,"score_spread":0.2096247741526425,"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."}}