{"id":"W3122901765","doi":"10.1287/mnsc.2018.3161","title":"Supply Chain Proximity and Product Quality","year":2019,"lang":"en","type":"article","venue":"Management Science","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Upstream (networking); Supply chain; Downstream (manufacturing); Automotive industry; Factory (object-oriented programming); Component (thermodynamics); Quality (philosophy); Product (mathematics); Upstream and downstream (DNA); Industrial organization; Business; Sample (material); Computer science; Marketing; Mathematics; Engineering; Telecommunications; Chemistry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002447034,0.0002326624,0.0002167023,0.0005939203,0.0004268017,0.0007320978,0.0008627233,0.00002335628,0.0003109115],"category_scores_gemma":[0.00006370323,0.0001927334,0.00004839008,0.00163009,0.000436045,0.002777602,0.001271374,0.0001094777,0.001017242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005499915,"about_ca_system_score_gemma":0.00001435063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003125901,"about_ca_topic_score_gemma":0.00002891135,"domain_scores_codex":[0.9970779,0.0000125397,0.0003068672,0.001015196,0.000942372,0.000645119],"domain_scores_gemma":[0.9989344,0.00001918525,0.0001752682,0.0007509619,0.00008861655,0.00003158025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003697086,0.0001723125,0.468123,0.0008390102,0.00002429371,0.00001691533,0.000105149,0.0001709825,0.001008771,0.4692943,0.003151223,0.05705708],"study_design_scores_gemma":[0.0008213987,0.00002120601,0.8529562,0.00007269322,0.00003964382,0.000001462582,0.00156788,0.004756695,0.0002208116,0.008101352,0.1308203,0.0006203514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8347822,0.000078037,0.0002449925,0.002724697,0.0007145804,0.001532171,8.39988e-7,0.0002072214,0.1597153],"genre_scores_gemma":[0.990742,0.00004424876,0.0005671069,0.002397274,0.0002267321,0.00005708227,0.000004505733,0.00001521724,0.005945855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4611929,"threshold_uncertainty_score":0.9997606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411920013351989,"score_gpt":0.2519639867606788,"score_spread":0.2378447866271589,"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."}}