{"id":"W7128374346","doi":"10.5281/zenodo.18442615","title":"Supply Chain Resilience Metrics: Optimization under Multi-Risk Shocks","year":2025,"lang":"en","type":"article","venue":"Open MIND","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Backup; Resilience (materials science); Supply chain; Metric (unit); Pipeline (software); Supply chain management; Service (business); Service provider; Service level","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":[0.006929061,0.001626805,0.001019677,0.001808614,0.0003971391,0.003306394,0.0009290352,0.001234476,0.00171931],"category_scores_gemma":[0.01890092,0.0003553729,0.0008293272,0.00222401,0.001804507,0.004452554,0.002265336,0.00228479,0.0001877679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002293347,"about_ca_system_score_gemma":0.001484145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002063681,"about_ca_topic_score_gemma":0.001273786,"domain_scores_codex":[0.996251,0.002014806,0.0002631709,0.0004770635,0.000749687,0.0002443161],"domain_scores_gemma":[0.9914184,0.005068205,0.001799403,0.0004594828,0.0009337948,0.0003207392],"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":[0.00005642998,0.00004653691,0.004991745,0.0003092784,0.000248825,0.00008042828,0.0001464306,0.8415874,0.0004809773,0.1072483,0.00210336,0.0427003],"study_design_scores_gemma":[0.00000956142,0.0001640078,0.003477295,0.0002940015,0.00006405185,0.00007095285,0.0002383506,0.7341481,0.000685038,0.2569812,0.003808082,0.00005932636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07611052,0.007187894,0.9006135,0.003749288,0.0001904836,0.0001674924,0.000637592,0.0003050989,0.01103809],"genre_scores_gemma":[0.938031,0.003217646,0.05654041,0.0002464148,0.0001514896,0.0001714124,0.0004070816,0.00007969738,0.00115483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006929061,"threshold_uncertainty_score":0.03664476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02473760877352575,"score_gpt":0.2880371661532722,"score_spread":0.2632995573797464,"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."}}