{"id":"W3210236285","doi":"","title":"Prepare for the Worst – Using Facility Location to Alleviate Product Recall Risks","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","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":"Recall; Product (mathematics); Supply chain; Facility location problem; Business; Risk analysis (engineering); Supply chain management; Computer science; Operations management; Operations research; 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":[0.002335964,0.0008783989,0.0004982834,0.001057044,0.0009103076,0.00219285,0.001732724,0.001703687,0.003563196],"category_scores_gemma":[0.006707011,0.0003334796,0.0005922836,0.0006845829,0.000966472,0.003637267,0.002773809,0.001030449,0.00065687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009914653,"about_ca_system_score_gemma":0.001985914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002657962,"about_ca_topic_score_gemma":0.002639901,"domain_scores_codex":[0.9984007,0.0007976437,0.00005059657,0.0002687614,0.0002426735,0.0002395315],"domain_scores_gemma":[0.9972981,0.0007788392,0.0009553693,0.0004358513,0.0003116866,0.0002202161],"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":[0.0005797116,0.0003339951,0.01358061,0.0003433241,0.0002214094,0.0009028249,0.0009659256,0.6460743,0.0131282,0.07900972,0.005167022,0.2396931],"study_design_scores_gemma":[0.0001679316,0.002226861,0.0112564,0.0003205864,0.0003113708,0.001350116,0.004230832,0.7879562,0.01663312,0.1333822,0.04190993,0.0002545798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1667547,0.0006451836,0.8097079,0.004874676,0.0001776901,0.0002738182,0.0001830219,0.001157032,0.01622601],"genre_scores_gemma":[0.9219659,0.0002444025,0.07573144,0.000171866,0.00004221614,0.00005959406,0.00004923025,0.00003421409,0.001701084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003563196,"threshold_uncertainty_score":0.0123539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05582429234409249,"score_gpt":0.2871533861132541,"score_spread":0.2313290937691617,"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."}}