{"id":"W2395424964","doi":"10.1080/03155986.2006.11732738","title":"OPtimally Balancing Large Assembly Lines: Updating Johnson S 1988 Fable Algorithm<sup>*</sup>","year":2006,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Fable; Computer science; Algorithm; Heuristic; Task (project management); Process (computing); Range (aeronautics); Selection (genetic algorithm); Verifiable secret sharing; Mathematical optimization; Mathematics; Artificial intelligence; Programming language; Engineering; Set (abstract data type)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002161294,0.000231988,0.000269918,0.0005481849,0.0006187792,0.001223239,0.0001949044,0.0002011196,0.00003203789],"category_scores_gemma":[0.0002395087,0.000224544,0.00004319831,0.0006119316,0.00003380232,0.003778309,0.0000943941,0.0003932818,0.0002364134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002495967,"about_ca_system_score_gemma":0.0001738687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005152338,"about_ca_topic_score_gemma":0.00002368906,"domain_scores_codex":[0.9970436,0.00007253211,0.0009958453,0.000168511,0.001114538,0.0006050037],"domain_scores_gemma":[0.9981931,0.0002171223,0.00009458062,0.0002246136,0.001143593,0.0001269225],"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":[0.000006899676,0.00001220381,0.001053217,0.0002147914,0.0000203241,0.000001417647,0.0004754773,0.9752044,0.000113104,0.01152709,0.009367008,0.002004074],"study_design_scores_gemma":[0.0005605998,0.00003426471,0.0009096805,0.0001090533,0.000003604379,0.00002400549,0.001219304,0.9045364,0.0001767616,0.0000128796,0.09218896,0.0002245278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1384924,0.0009554545,0.7581713,0.0002846004,0.0006748246,0.002428722,0.0004333205,0.0009321476,0.0976272],"genre_scores_gemma":[0.9780719,0.0001055373,0.01820785,0.00008284219,0.0007777819,0.0002282815,0.001619863,0.00004381089,0.0008621001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8395795,"threshold_uncertainty_score":0.9998136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01484369503608432,"score_gpt":0.2775481892404374,"score_spread":0.262704494204353,"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."}}