{"id":"W102879083","doi":"10.19030/ijmis.v16i2.6913","title":"An Operational Framework For Reverse Supply Chains","year":2012,"lang":"en","type":"article","venue":"International Journal of Management & Information Systems (IJMIS)","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"DePaul University","keywords":"Supply chain; Reverse logistics; Business; Industrial organization; Supply chain management; Service management; Field survey; Product (mathematics); Marketing; Operations management; Economics; Engineering","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":["scholarly_communication"],"category_scores_codex":[0.002532453,0.0002732545,0.0002975304,0.001453324,0.0002001276,0.001442159,0.001112394,0.00010335,0.0002881698],"category_scores_gemma":[0.0002289673,0.0002572618,0.0002078014,0.0003505081,0.00003657912,0.0152058,0.0002190632,0.0001908349,0.0002941981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000384684,"about_ca_system_score_gemma":0.0000356889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007442686,"about_ca_topic_score_gemma":0.000001695924,"domain_scores_codex":[0.9965594,0.00003274438,0.001323161,0.0001444366,0.001489031,0.0004511954],"domain_scores_gemma":[0.9962279,0.00008673084,0.001438363,0.0003014304,0.001880155,0.00006546456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002493793,0.0002220059,0.007017818,0.0004543722,0.0005103703,0.00001350606,0.0005407092,0.0117299,0.000005613553,0.9208785,0.05032273,0.008055131],"study_design_scores_gemma":[0.001843315,0.00004114,0.005788374,0.000250106,0.0001296387,0.00003123388,0.01167999,0.01450366,0.000009447959,0.002603975,0.9627572,0.0003619236],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02265614,0.0002648865,0.9107307,0.006764242,0.01821911,0.002892648,0.00006165147,0.000213401,0.03819722],"genre_scores_gemma":[0.9783832,0.00004248495,0.007797185,0.005717519,0.006851511,0.0001451803,0.0002672923,0.00003402644,0.0007615347],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9557271,"threshold_uncertainty_score":0.999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01332434597676534,"score_gpt":0.2665467202309416,"score_spread":0.2532223742541763,"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."}}