{"id":"W2037573222","doi":"10.1080/00207543.2014.974839","title":"Developing assembly line layout for delayed product differentiation using phylogenetic networks","year":2014,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Tracing; Product (mathematics); Modular design; Computer science; Flexibility (engineering); Metric (unit); New product development; Engineering; Mathematics; Operations management","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":[0.003570833,0.0001365506,0.0001840245,0.0009455343,0.0002592628,0.0003824778,0.0004714047,0.00005735296,0.00002892031],"category_scores_gemma":[0.002518239,0.0001229167,0.0000800159,0.0005647611,0.0000493194,0.001039383,0.0001282825,0.0002592843,0.00001582074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002161034,"about_ca_system_score_gemma":0.0001606465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002529392,"about_ca_topic_score_gemma":0.00001575465,"domain_scores_codex":[0.9977568,0.00006812463,0.0005948922,0.0003029671,0.0009858637,0.0002913779],"domain_scores_gemma":[0.993481,0.00007511383,0.0005286658,0.000149514,0.005747641,0.00001804676],"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":[0.004222554,0.001082748,0.04755306,0.0005683176,0.001638717,0.00001819815,0.0005624946,0.3499166,0.1433495,0.08047051,0.03352396,0.3370934],"study_design_scores_gemma":[0.005496316,0.0002666966,0.04617597,0.0008581589,0.0002907824,0.0001976766,0.000512788,0.6687017,0.04831084,0.0433474,0.1844465,0.001395163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6568877,0.0001591747,0.3249824,0.01114694,0.006048361,0.0005098173,8.405115e-7,0.00003542182,0.0002293428],"genre_scores_gemma":[0.9775725,0.00003128133,0.005828572,0.0001756443,0.01607271,0.00001466342,0.00004589036,0.00002983139,0.0002289021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3356982,"threshold_uncertainty_score":0.5012395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1062564231862808,"score_gpt":0.3682655102730011,"score_spread":0.2620090870867203,"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."}}