{"id":"W2006750788","doi":"10.1016/j.cor.2005.03.008","title":"Assessing performance and uncertainty in developing carpet reverse logistics systems","year":2005,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":174,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Reverse logistics; Incentive; Memorandum; Computer science; Supply chain; Government (linguistics); Variable (mathematics); Operations research; Business; Environmental economics; Economics; Marketing; Engineering; Microeconomics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008072405,0.0007345966,0.0004832759,0.001379698,0.0008473098,0.002195091,0.0007677316,0.001168597,0.0006655012],"category_scores_gemma":[0.02464555,0.0004661159,0.000507549,0.001029061,0.0006341007,0.002537356,0.0009734032,0.0009957517,0.0001107317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005100249,"about_ca_system_score_gemma":0.002738476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02151767,"about_ca_topic_score_gemma":0.01404989,"domain_scores_codex":[0.9974622,0.001115612,0.0002168328,0.0002226391,0.000607382,0.0003752951],"domain_scores_gemma":[0.9808118,0.01482,0.0012253,0.0004180343,0.002330799,0.0003939094],"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":[0.0007382445,0.0003278293,0.02742763,0.000052815,0.00006613155,0.00008637667,0.0002782161,0.9421222,0.003573964,0.001377009,0.00008628603,0.02386327],"study_design_scores_gemma":[0.00002571751,0.0009839296,0.009805653,0.00001067574,0.00004211916,0.00001534607,0.000302432,0.9777063,0.01017328,0.0008173065,0.00009048186,0.00002686142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918004,0.00002342501,0.00718358,0.00004201289,0.000002614483,0.00003911191,0.00002673916,0.00002654183,0.0008555386],"genre_scores_gemma":[0.9961124,0.00001443469,0.003656093,0.000003139035,0.000001057269,0.00001532872,0.00004917213,0.000002608374,0.0001458582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02151767,"threshold_uncertainty_score":0.04278481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09024852783511896,"score_gpt":0.350061384722287,"score_spread":0.2598128568871681,"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."}}