{"id":"W3106549497","doi":"10.5296/ber.v10i4.18024","title":"Prepare for the Worst - Using Facility Location to Alleviate Product Recall Risks","year":2020,"lang":"en","type":"article","venue":"Business and Economic Research","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Product (mathematics); Recall; Supply chain; Facility location problem; Computer science; Business; Risk analysis (engineering); Operations research; Operations management; Environmental economics; Marketing; Economics; Engineering; Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002332525,0.0008449015,0.0004751289,0.0009715456,0.0008718561,0.001750867,0.001806099,0.001742074,0.00405265],"category_scores_gemma":[0.007664467,0.0003678253,0.0005209806,0.0006738057,0.0007738176,0.003161053,0.002012602,0.00115206,0.0007129728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197374,"about_ca_system_score_gemma":0.002458668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002995874,"about_ca_topic_score_gemma":0.003565013,"domain_scores_codex":[0.9983354,0.00085447,0.00005234842,0.0002495071,0.0002597102,0.0002485869],"domain_scores_gemma":[0.9974112,0.0007466766,0.0008217811,0.0004026307,0.0004244715,0.0001932929],"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":[0.0003811559,0.0002364127,0.009976434,0.0002375497,0.000118848,0.0006101377,0.0006060796,0.7419423,0.01131796,0.07163702,0.005611762,0.1573242],"study_design_scores_gemma":[0.0001167218,0.001106945,0.007293052,0.0001974266,0.0001442387,0.0006717758,0.002907225,0.855901,0.01300214,0.08874228,0.02975274,0.0001644677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.103577,0.0003684952,0.880137,0.00328371,0.0001547212,0.000246048,0.0001804553,0.0009597039,0.01109285],"genre_scores_gemma":[0.8956461,0.0002207092,0.1017998,0.0001497606,0.00004189569,0.00008022824,0.00006904337,0.00004723311,0.001945198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00405265,"threshold_uncertainty_score":0.01355743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2773667362101103,"score_gpt":0.3825393474791929,"score_spread":0.1051726112690826,"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."}}