{"id":"W4406035968","doi":"10.54254/2754-1169/2024.19201","title":"Advanced Computing in Supply Chain Management","year":2025,"lang":"en","type":"article","venue":"Advances in Economics Management and Political Sciences","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Supply chain; Supply chain management; Transparency (behavior); Risk analysis (engineering); Emerging technologies; Profitability index; Service management; Process management; Computer science; Supply chain risk management; Business; Knowledge management; Marketing; Computer security","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":[0.001431416,0.000463943,0.0004336408,0.001861136,0.001043176,0.005225345,0.000697422,0.001686113,0.00488542],"category_scores_gemma":[0.003823537,0.0002440801,0.0003590836,0.005000818,0.002486038,0.005078216,0.002150049,0.001675749,0.001097759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002401904,"about_ca_system_score_gemma":0.002069923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421599,"about_ca_topic_score_gemma":0.001829997,"domain_scores_codex":[0.9984725,0.0006862999,0.0000887743,0.0001920292,0.0004433976,0.0001169721],"domain_scores_gemma":[0.998019,0.001094589,0.0002520461,0.0002562373,0.0002614621,0.0001166248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002832496,0.00003346675,0.001320708,0.0004425317,0.00003164341,0.0001983389,0.000513608,0.02092346,0.0004392722,0.8083605,0.009513002,0.1581952],"study_design_scores_gemma":[0.000008893685,0.00003808094,0.0008354493,0.0006056023,0.00001981774,0.0001898712,0.000660342,0.03193095,0.0005260127,0.7777231,0.18743,0.00003186692],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03319426,0.2301159,0.3457143,0.04358167,0.002978992,0.0002879725,0.0002674001,0.0004299747,0.3434296],"genre_scores_gemma":[0.6794943,0.1674874,0.1107912,0.00256472,0.003157583,0.0002268448,0.0002306675,0.0001068758,0.03594054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005225345,"threshold_uncertainty_score":0.01742709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006486900188264643,"score_gpt":0.2557920695553318,"score_spread":0.2493051693670672,"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."}}