{"id":"W4388311732","doi":"10.5267/j.uscm.2023.9.013","title":"Customs intelligence and risk management in sustainable supply chain for general customs department logistics","year":2023,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Supply Chain Resilience and Risk Management","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Supply chain; Supply chain management; Sustainability; Supply chain risk management; Risk management; Context (archaeology); Sustainable development; Marketing; Service management; Finance","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":[0.002489215,0.0003292318,0.0001742575,0.001437643,0.001588209,0.003681539,0.0003817604,0.0004211862,0.003700712],"category_scores_gemma":[0.006106981,0.0001619215,0.0003217256,0.002026785,0.001777566,0.002315155,0.002126249,0.0008280216,0.0002279183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002860841,"about_ca_system_score_gemma":0.004157758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005229782,"about_ca_topic_score_gemma":0.00794553,"domain_scores_codex":[0.9981694,0.0007599264,0.0001576664,0.0001910338,0.0004891734,0.0002327491],"domain_scores_gemma":[0.9921004,0.002556593,0.003294534,0.0003638297,0.00120997,0.000474528],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001449097,0.0004792399,0.7612792,0.0004601365,0.0001004143,0.001839125,0.09648772,0.004637085,0.00191014,0.02015839,0.001358502,0.1111453],"study_design_scores_gemma":[0.00001476799,0.0004101914,0.5996134,0.0005718437,0.0001098503,0.001388607,0.3349513,0.009000013,0.002908791,0.01907071,0.03182423,0.0001363689],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865306,0.0002103619,0.001997924,0.0005405121,0.000006020997,0.0000551452,0.00005043839,0.00001073965,0.01059824],"genre_scores_gemma":[0.9975953,0.0001539033,0.001051222,0.00004967982,0.000005170687,0.00001570317,0.00002822592,0.000002735185,0.001098182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005229782,"threshold_uncertainty_score":0.02075696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01850271686349097,"score_gpt":0.2601010213362105,"score_spread":0.2415983044727196,"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."}}