{"id":"W4296436687","doi":"10.2139/ssrn.4222683","title":"Tail Risk in Production Networks","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Flexible and Reconfigurable Manufacturing Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Production (economics); Business; Economics; 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":[0.003667653,0.0009228676,0.001854151,0.001728276,0.001042854,0.003154771,0.001550479,0.002990076,0.01334957],"category_scores_gemma":[0.0279602,0.0008417143,0.0007202622,0.001562856,0.002898459,0.007270603,0.002196645,0.003699515,0.001196332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001995221,"about_ca_system_score_gemma":0.0008424067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002170467,"about_ca_topic_score_gemma":0.001576664,"domain_scores_codex":[0.9986865,0.0005177281,0.00005212727,0.0002624607,0.0002501939,0.0002308784],"domain_scores_gemma":[0.9822834,0.0128467,0.001852849,0.0009837167,0.001042749,0.0009905403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002195769,0.00008489797,0.003208448,0.0001336488,0.00005376681,0.0003033681,0.000248365,0.1924124,0.0006788856,0.7711384,0.008272843,0.02324541],"study_design_scores_gemma":[0.00001464574,0.00004302414,0.0008610767,0.00004150095,0.0000232743,0.000112586,0.00008146161,0.3121104,0.0002174897,0.6850813,0.001388059,0.00002511953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2974918,0.00341017,0.6220421,0.008948107,0.0003819215,0.0001217969,0.0006110283,0.0008699497,0.06612324],"genre_scores_gemma":[0.9598272,0.001293601,0.004816984,0.0003036737,0.000227053,0.00006245534,0.0001556388,0.0001040768,0.03320937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01334957,"threshold_uncertainty_score":0.04465872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003928951200071126,"score_gpt":0.1753416625709416,"score_spread":0.1714127113708705,"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."}}