{"id":"W4402847098","doi":"10.5267/j.ijiec.2024.7.002","title":"Coordination and optimization decision of assembly building supply chain under supply disruption risk","year":2024,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Office for Philosophy and Social Sciences","keywords":"Supply chain; Supply chain risk management; Supply chain optimization; Business; Risk analysis (engineering); Operations management; Computer science; Mathematical optimization; Manufacturing engineering; Engineering; Supply chain management; Service management; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002294175,0.0009081318,0.001382952,0.00082621,0.0008641116,0.002159083,0.001156799,0.001179803,0.004011196],"category_scores_gemma":[0.003776699,0.0008149438,0.001029947,0.00108396,0.001051174,0.001491563,0.001397645,0.001224095,0.0002817779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002590528,"about_ca_system_score_gemma":0.002887205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01507747,"about_ca_topic_score_gemma":0.006628696,"domain_scores_codex":[0.998583,0.0005612123,0.00005119305,0.0003111829,0.0001542534,0.0003392145],"domain_scores_gemma":[0.9975325,0.001317929,0.0005064018,0.0001172912,0.0002677291,0.0002581851],"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":[0.00007650622,0.00002054496,0.0006074875,0.00002392179,0.00001606129,0.00006596796,0.00003729042,0.9921967,0.000427342,0.0035915,0.0001172056,0.002819463],"study_design_scores_gemma":[0.00001441701,0.00004950348,0.0002634806,0.000004301029,0.000011237,0.00001175088,0.00004017885,0.9960129,0.0002482919,0.003201154,0.0001355049,0.000007260427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3204567,0.000342328,0.6672437,0.0005276066,0.00003994112,0.0002512057,0.0002144626,0.00015378,0.01077028],"genre_scores_gemma":[0.9746386,0.0001740861,0.02162434,0.00002863457,0.000009101743,0.0001272448,0.00009581673,0.00001797677,0.003284223],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01507747,"threshold_uncertainty_score":0.02997941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520935088360698,"score_gpt":0.2641820026672763,"score_spread":0.2489726517836693,"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."}}