{"id":"W4378191850","doi":"10.1109/syscon53073.2023.10131063","title":"Counterfeit Detection in the e-Commerce Industry Using Machine Learning: A Review","year":2023,"lang":"en","type":"review","venue":"","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Safeguarding; Credit card; E-commerce; Counterfeit; Credit card fraud; Work (physics); Data science; Computer security; Machine learning; World Wide Web; Engineering","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":[0.001659916,0.0008633409,0.001320885,0.005111479,0.00036455,0.001231576,0.001263126,0.001428139,0.003006144],"category_scores_gemma":[0.004150767,0.0004601552,0.0008204713,0.004511868,0.0005798637,0.002176691,0.0006575544,0.001232096,0.001715326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006333378,"about_ca_system_score_gemma":0.001938347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001983823,"about_ca_topic_score_gemma":0.002778814,"domain_scores_codex":[0.9994423,0.000113485,0.00008167156,0.00009597041,0.0002304895,0.00003607023],"domain_scores_gemma":[0.9964754,0.002239849,0.0002810726,0.00007209035,0.0008329988,0.00009868582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003978257,0.00007973525,0.0005927244,0.01988121,0.0001150263,0.00008847268,0.00007210029,0.0005740774,0.0003721901,0.002814017,0.01956794,0.9558027],"study_design_scores_gemma":[0.00001872588,0.0002745827,0.003018789,0.02420447,0.000559548,0.001167587,0.0002395316,0.0009330822,0.0008409761,0.00446111,0.9642135,0.00006814954],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000224815,0.9975005,0.0004954381,0.0003958339,0.0001961496,0.00001298994,0.0000285151,0.00001504935,0.001130775],"genre_scores_gemma":[0.001425879,0.9973469,0.0005206082,0.0002104476,0.0001523462,0.000009742092,0.00004050396,0.000003222869,0.0002904113],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005111479,"threshold_uncertainty_score":0.0100565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1665894373082538,"score_gpt":0.375893145124954,"score_spread":0.2093037078167002,"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."}}