{"id":"W7126388153","doi":"10.21428/594757db.fca1c492","title":"Handling Concept Drift in Fraud Detection: A Replication Study","year":2025,"lang":"en","type":"article","venue":"","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Replication (statistics); Replicate; Concept drift; Recall; Simple (philosophy)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003455145,0.00006294341,0.00008606194,0.0001663921,0.00005233307,0.0001078802,0.000621271,0.0000325383,0.000005595099],"category_scores_gemma":[0.0001143588,0.0000604088,0.00001372071,0.0006652982,0.0000145769,0.0002909773,0.0002878596,0.00008637289,0.00000742559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004050717,"about_ca_system_score_gemma":0.00003278067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002622515,"about_ca_topic_score_gemma":0.0002602715,"domain_scores_codex":[0.9991586,0.00004708871,0.0001762045,0.0004196537,0.00009384096,0.0001046607],"domain_scores_gemma":[0.9987371,0.00006347431,0.0000346742,0.001113295,0.00003561683,0.0000158649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007023534,0.0004075418,0.03111202,0.000006640084,0.00001817153,0.00001222589,0.002388622,0.00001719429,0.001890531,0.04087905,0.003868507,0.9193925],"study_design_scores_gemma":[0.002895708,0.001298772,0.2865563,0.00026046,0.0000260773,0.00001760573,0.002021684,0.1648572,0.4764495,0.03665072,0.02797372,0.0009921881],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05298338,0.00002055943,0.938589,0.0003691351,0.0001040922,0.0002944563,3.968924e-7,0.0006164076,0.007022587],"genre_scores_gemma":[0.9487904,0.000001221008,0.05054894,0.0001783835,0.000009411837,0.00007033683,9.65476e-7,0.000002090613,0.000398189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9184003,"threshold_uncertainty_score":0.2463399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01286573610785745,"score_gpt":0.3056526419245791,"score_spread":0.2927869058167216,"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."}}