{"id":"W2752264660","doi":"10.1080/19397038.2017.1370032","title":"Performance evaluation of reverse logistics enterprise – an agent-based simulation approach","year":2017,"lang":"en","type":"article","venue":"International Journal of Sustainable Engineering","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal; Université Laval","keywords":"Remanufacturing; Reverse logistics; Reuse; Computer science; Process (computing); Sorting; Quality (philosophy); Point (geometry); Manufacturing engineering; Process management; Supply chain; Risk analysis (engineering); Operations research; Business; Engineering; Marketing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001053481,0.0008798359,0.0008817607,0.0008176211,0.0005612636,0.001371805,0.001055876,0.001377602,0.001725436],"category_scores_gemma":[0.001732243,0.0003194255,0.0008971086,0.0005062917,0.0004644868,0.0007669174,0.0007514507,0.0006443675,0.0001664871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001320751,"about_ca_system_score_gemma":0.001295753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01694776,"about_ca_topic_score_gemma":0.007687571,"domain_scores_codex":[0.9994986,0.0002642797,0.00002588839,0.00005273601,0.00008198075,0.0000765743],"domain_scores_gemma":[0.998776,0.0007625479,0.0001168875,0.00005493568,0.0002181575,0.00007141625],"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":[0.00003872918,0.00004497194,0.0007845723,0.00001598057,0.00001468422,0.00002948401,0.0000234996,0.9964085,0.0003571717,0.0009213918,0.00005698302,0.00130407],"study_design_scores_gemma":[0.000004570742,0.00002508187,0.00009106679,0.000002029878,0.000005762771,0.000002562712,0.00001169361,0.9994496,0.0001420629,0.000184162,0.00007857631,0.000002812362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.773969,0.000399296,0.1983193,0.0006314757,0.00008084974,0.0003141682,0.000362801,0.0004723947,0.02545077],"genre_scores_gemma":[0.9827769,0.0001455611,0.01519554,0.00002322205,0.000006814897,0.0001282135,0.0001192071,0.00001332932,0.001591258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01694776,"threshold_uncertainty_score":0.0336982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02991050720098289,"score_gpt":0.2788016556624865,"score_spread":0.2488911484615036,"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."}}