{"id":"W7056098176","doi":"","title":"Dynamic Bayesian network modelling of supply chain resilience : learning from multiple cases","year":2015,"lang":"en","type":"article","venue":"Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Supply chain; Dynamic Bayesian network; Resilience (materials science); Bayesian network; Conceptualization; Supply chain risk management; Variable (mathematics); Supply chain network; Vulnerability (computing); Bayesian probability","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009623553,0.001027789,0.001245266,0.002442789,0.001003709,0.002414716,0.002415936,0.002471968,0.005024734],"category_scores_gemma":[0.04253561,0.001210184,0.001826155,0.002147587,0.001891724,0.004885633,0.002603439,0.003506678,0.0003983253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003982229,"about_ca_system_score_gemma":0.001880593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03218687,"about_ca_topic_score_gemma":0.03189808,"domain_scores_codex":[0.9967933,0.001813434,0.0001684395,0.0005656556,0.0003724025,0.0002867107],"domain_scores_gemma":[0.9584672,0.03578955,0.002473947,0.001178774,0.001568636,0.0005218234],"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":[0.00004030821,0.00002959024,0.002266048,0.0000290271,0.00003254573,0.0001039053,0.0001507664,0.972643,0.00007592235,0.01921462,0.0002682962,0.005145945],"study_design_scores_gemma":[0.000006700941,0.000009940831,0.0002393667,0.00001437573,0.000009813572,0.00001315378,0.00004936779,0.9767082,0.00006660735,0.02257205,0.0003017124,0.000008697669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1796398,0.0003700835,0.8072884,0.002578638,0.00005460161,0.0002686074,0.001156618,0.0002309418,0.008412258],"genre_scores_gemma":[0.8797587,0.0004545566,0.1144414,0.0001664325,0.00005369963,0.0004387544,0.0009106104,0.00005853919,0.003717332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03218687,"threshold_uncertainty_score":0.06399906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02081348797122506,"score_gpt":0.2001796854741804,"score_spread":0.1793661975029553,"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."}}