{"id":"W1974331289","doi":"10.1016/j.jmsy.2007.10.004","title":"Tool planning for a lights-out machining system","year":2007,"lang":"en","type":"article","venue":"Journal of Manufacturing Systems","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Randomness; Computer science; Machining; Machine tool; Erlang (programming language); Set (abstract data type); Reliability engineering; Industrial engineering; Engineering; Mechanical engineering; Mathematics","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":[0.001342944,0.0002291204,0.0004291677,0.0003266801,0.0001279244,0.0001571778,0.0002422215,0.0001365204,0.000003660748],"category_scores_gemma":[0.00002864133,0.0001911262,0.0001536612,0.00004303101,0.000009608565,0.0002098424,0.00001717779,0.0002663702,0.000005298592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002302051,"about_ca_system_score_gemma":0.00001996103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003662179,"about_ca_topic_score_gemma":0.000001046809,"domain_scores_codex":[0.9981798,0.00001862265,0.000948031,0.0001311849,0.0003365135,0.0003858327],"domain_scores_gemma":[0.9990368,0.0001796643,0.0004064341,0.0001571636,0.0001003481,0.000119586],"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":[0.00008823913,0.00001173427,0.0001768626,0.002430168,0.0001706054,0.00008267469,0.001183382,0.991453,0.0003575775,0.0002022212,0.0006668441,0.003176706],"study_design_scores_gemma":[0.005599418,0.0006611684,0.004548773,0.008587376,0.0004456434,0.002602696,0.005676055,0.2920596,0.5587353,0.0001477867,0.1187876,0.002148573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4710581,0.001218209,0.5214965,0.000009723864,0.003741755,0.0002707914,0.000004748917,0.0002197365,0.001980389],"genre_scores_gemma":[0.995331,0.00000903747,0.003173562,0.000008923736,0.001223007,0.000007257911,0.000002290689,0.00006255533,0.0001823805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6993934,"threshold_uncertainty_score":0.7793899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529971183548909,"score_gpt":0.238237207359608,"score_spread":0.2229374955241189,"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."}}