{"id":"W3133678304","doi":"10.3390/e23030300","title":"Predicting Fraud Victimization Using Classical Machine Learning","year":2021,"lang":"en","type":"article","venue":"Entropy","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Investment (military); Business; Actuarial science; Finance; Political science; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001497151,0.0003730047,0.0003949619,0.003353154,0.0006655272,0.001288607,0.0005738398,0.0006098851,0.001202551],"category_scores_gemma":[0.009489755,0.0001403901,0.0004115331,0.00155199,0.0005063863,0.0006956149,0.0005523428,0.0006786253,0.0002870368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002388886,"about_ca_system_score_gemma":0.002387772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1366542,"about_ca_topic_score_gemma":0.1226873,"domain_scores_codex":[0.9992472,0.0001911325,0.00007200546,0.0001063853,0.0002147899,0.0001686475],"domain_scores_gemma":[0.9956027,0.002230236,0.0007726194,0.0002648153,0.0008117448,0.0003179324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007066545,0.0002861964,0.9426855,0.00002284272,0.00006188286,0.0001189913,0.0001064613,0.01991747,0.0001345955,0.0006229632,0.001462215,0.03451025],"study_design_scores_gemma":[0.00001063514,0.0001094869,0.4633326,0.00005142094,0.00003682087,0.0001604164,0.0006150432,0.5308875,0.0005989487,0.002653858,0.001517536,0.00002580092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918147,0.0002120009,0.004529139,0.0004955971,0.00001661039,0.00007402831,0.001040938,0.00005184062,0.001765204],"genre_scores_gemma":[0.9964552,0.0001003637,0.002126719,0.0000342819,0.00001219658,0.00001530969,0.0009040795,0.00000164772,0.0003502314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1366542,"threshold_uncertainty_score":0.2717176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01804634277638277,"score_gpt":0.2500041055609675,"score_spread":0.2319577627845847,"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."}}