{"id":"W2089305940","doi":"10.1115/1.1826075","title":"Optimal Module Selection for Preliminary Design of Reconfigurable Machine Tools","year":2005,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Set (abstract data type); Construct (python library); Component (thermodynamics); Space (punctuation); Feature (linguistics); Engineering drawing; Artificial intelligence; Programming language; Engineering; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006737073,0.0001195374,0.0001846018,0.0002932001,0.0000757815,0.00007275426,0.0001626055,0.00004405845,0.000009227162],"category_scores_gemma":[0.00008392894,0.0001084217,0.00003695825,0.0001130452,0.00002873178,0.0009191673,0.00001104649,0.0001298626,3.245254e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000787364,"about_ca_system_score_gemma":0.00003499334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001410848,"about_ca_topic_score_gemma":1.695407e-7,"domain_scores_codex":[0.999134,0.00000350713,0.000319855,0.0001027899,0.0002189734,0.0002208781],"domain_scores_gemma":[0.9995642,0.00007002206,0.00009935859,0.0000643102,0.000120711,0.00008133936],"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":[0.00002632299,0.000007932239,0.000005920047,0.0001382028,0.00001258671,3.41193e-7,0.0001076147,0.9614953,0.01364496,0.000007700017,0.00003311226,0.02451994],"study_design_scores_gemma":[0.0001612411,0.0001043546,0.0004140633,0.00004817162,0.000010537,0.00003589498,0.000008610774,0.5679728,0.4306175,0.000007495107,0.0005462924,0.00007308504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4304519,0.0003214766,0.5688247,0.00003532968,0.000167103,0.0001049656,0.000001671352,0.00003814339,0.00005473915],"genre_scores_gemma":[0.9139564,0.0001587729,0.0857258,0.000005932092,0.0001092575,0.000004258267,3.997765e-7,0.00001613481,0.00002303697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4835045,"threshold_uncertainty_score":0.4421309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01395460516650828,"score_gpt":0.2109646600061553,"score_spread":0.197010054839647,"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."}}