{"id":"W4401455142","doi":"10.18357/bigr52202421689","title":"Customs Laboratories and the Prevention and Detection of Customs Fraud: Two Case Studies","year":2024,"lang":"en","type":"article","venue":"Borders in Globalization Review","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Business; Computer security; Computer science","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":[0.01424603,0.0004947473,0.0005070865,0.004816964,0.002627025,0.002860118,0.001245286,0.002672067,0.001685498],"category_scores_gemma":[0.02874822,0.0002826475,0.0008122489,0.006494349,0.002346571,0.003110544,0.002376849,0.001387289,0.0002376063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003555289,"about_ca_system_score_gemma":0.002587991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009598739,"about_ca_topic_score_gemma":0.01352002,"domain_scores_codex":[0.9894309,0.007074755,0.0007416001,0.000401617,0.001681368,0.0006698013],"domain_scores_gemma":[0.9685982,0.02122132,0.004419177,0.001927779,0.003280194,0.0005533314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"case_report","study_design_scores_codex":[0.0008826135,0.002053363,0.2389599,0.004729348,0.0004677227,0.02845728,0.03456156,0.01687409,0.001487715,0.1282895,0.02995057,0.5132864],"study_design_scores_gemma":[0.0003816011,0.001994798,0.2529581,0.01464579,0.001204544,0.04302118,0.144219,0.05889411,0.01538882,0.05140683,0.415448,0.0004373289],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8903168,0.03713132,0.02659577,0.01052577,0.0002880354,0.001076882,0.0005316217,0.00007397157,0.03345968],"genre_scores_gemma":[0.9582652,0.01893391,0.01818325,0.0006970905,0.0001104274,0.0002007086,0.0002295126,0.00001806274,0.003361795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01424603,"threshold_uncertainty_score":0.07534111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01637705791534722,"score_gpt":0.358904514955055,"score_spread":0.3425274570397078,"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."}}