{"id":"W2479250721","doi":"10.1007/s00170-016-9141-z","title":"A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems","year":2016,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Remanufacturing; Cellular manufacturing; Control reconfiguration; Sustainability; Production planning; Process (computing); Manufacturing engineering; Integer programming; Production (economics); Computer science; Hybrid system; Engineering; Industrial engineering; Systems engineering; Mathematical optimization; Embedded system; 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":[0.0004714223,0.0003763223,0.0004786951,0.0005441963,0.0001488537,0.0001009163,0.001353093,0.0001638892,0.0000306515],"category_scores_gemma":[0.0002528743,0.0002393232,0.0001932536,0.00004363815,0.0001580768,0.0004949103,0.0000952835,0.0004038912,0.00001533036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003992495,"about_ca_system_score_gemma":0.00004038577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.503107e-7,"about_ca_topic_score_gemma":9.283557e-7,"domain_scores_codex":[0.9978152,0.00002152678,0.0009136929,0.0002801499,0.0004453639,0.0005240972],"domain_scores_gemma":[0.9982184,0.0004853839,0.0004934808,0.0004427363,0.0002593581,0.0001007076],"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":[0.0001148621,0.00002423522,0.000001212009,0.00005860492,0.0002105487,0.00003359233,0.00004670248,0.9328243,0.03840128,0.001935382,0.0003346702,0.02601457],"study_design_scores_gemma":[0.001025082,0.0000683695,0.00000292839,0.0002727824,0.0000470364,0.0002955261,0.00008531472,0.06040205,0.8377768,0.09749096,0.00225051,0.0002826064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1356082,0.0002970748,0.8606429,0.0009734049,0.001359821,0.0003449081,0.00002773019,0.0003698986,0.0003760684],"genre_scores_gemma":[0.9507991,0.0004622562,0.04776474,0.00003847347,0.0002775443,0.00006056972,0.000004195937,0.00009338509,0.0004997237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8724223,"threshold_uncertainty_score":0.9759316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01414011329478441,"score_gpt":0.2283486255631208,"score_spread":0.2142085122683364,"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."}}