{"id":"W1989356260","doi":"10.1109/icsssm.2013.6602607","title":"A fundamental flexibility measure: Machine flexibility","year":2013,"lang":"en","type":"article","venue":"","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Flexibility (engineering); Measure (data warehouse); Computer science; Dependency (UML); Machine tool; Probabilistic logic; Risk analysis (engineering); Industrial engineering; Manufacturing engineering; Artificial intelligence; Engineering; Data mining; Business; 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005491798,0.0001919182,0.0001735094,0.000112861,0.0001967653,0.0003653912,0.0002248104,0.0000588523,0.01000447],"category_scores_gemma":[0.0001493712,0.0001554094,0.00006781179,0.0004388845,0.00005871994,0.002029192,0.000184209,0.0001049049,0.004830583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007018787,"about_ca_system_score_gemma":0.00002511058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00115932,"about_ca_topic_score_gemma":0.0001470554,"domain_scores_codex":[0.9986197,0.00001159778,0.0003098534,0.0004369209,0.0003217218,0.0003001748],"domain_scores_gemma":[0.9992771,0.00001202427,0.0001039112,0.0003647366,0.0002239497,0.0000182548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001792965,0.0009343901,0.6780491,0.0003580134,0.0001149378,0.000003484618,0.0001566707,0.000051365,0.005931373,0.04503395,0.09580465,0.1733827],"study_design_scores_gemma":[0.00289556,0.0000231653,0.7235914,0.00004379663,0.00009060553,0.000004565854,0.0004595675,0.02971473,0.002020977,0.0737346,0.1658252,0.001595804],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7630556,0.0001364395,0.002340009,0.004989599,0.0008036058,0.001191304,0.000001714556,0.0008674083,0.2266143],"genre_scores_gemma":[0.9927757,0.000001465797,0.0006380211,0.002691912,0.0004878339,0.00005372144,0.0000717997,0.00001862733,0.003260854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2297201,"threshold_uncertainty_score":0.9959443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693273130999302,"score_gpt":0.2253530030371405,"score_spread":0.1984202717271475,"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."}}