{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002228102,0.0001558723,0.0001937585,0.0007654881,0.000134314,0.0004100541,0.0008842445,0.00005154348,0.00006179239],"category_scores_gemma":[0.001582948,0.0001588186,0.00009724728,0.0001196113,0.00004094933,0.002973956,0.0001951724,0.0001469734,0.000003021306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004520802,"about_ca_system_score_gemma":0.0001078987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121045,"about_ca_topic_score_gemma":9.155796e-7,"domain_scores_codex":[0.9979534,0.00001485616,0.0004909892,0.0001478342,0.001150511,0.0002423894],"domain_scores_gemma":[0.9947675,0.00003907484,0.0009972656,0.0003227837,0.003851616,0.00002177425],"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":[0.0001029584,0.0001239878,0.006626897,0.0002511423,0.00008244547,0.00006277204,0.00005968557,0.9853723,0.00006020422,0.00308475,0.0000995718,0.004073289],"study_design_scores_gemma":[0.001312305,0.00002661423,0.0127918,0.00008573633,0.0001277395,0.000002682415,0.00147256,0.9782993,0.00007230461,0.0002488414,0.005413601,0.0001465441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7989027,0.00005318694,0.1954661,0.0002915201,0.001198566,0.0004471817,0.000001559054,0.00003612129,0.003603027],"genre_scores_gemma":[0.9970068,0.000004682721,0.001826123,0.00007243344,0.0009091393,0.000009538576,0.00001573694,0.00002608676,0.0001294487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1981041,"threshold_uncertainty_score":0.6476433,"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."}}