{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008818068,0.00008353811,0.0001048235,0.00002992133,0.0003099088,0.000103374,0.000138441,0.00002904595,0.00009016357],"category_scores_gemma":[0.00009526723,0.00008001768,0.00004642321,0.0002349266,0.00001560333,0.0002423553,0.0002669921,0.0001452706,0.00002328069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004366256,"about_ca_system_score_gemma":0.00003318564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004789009,"about_ca_topic_score_gemma":0.00001021942,"domain_scores_codex":[0.9991674,0.00006268384,0.0001485481,0.0002276857,0.0001984255,0.0001952867],"domain_scores_gemma":[0.9996341,0.00004941782,0.00004694841,0.0001680653,0.00005878157,0.00004268631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000259364,0.0003313335,0.2899246,0.00007945421,0.0003253263,0.0002726507,0.007141832,0.1037353,0.03865571,0.533054,0.001671081,0.02478281],"study_design_scores_gemma":[0.0003076082,0.00002802088,0.002177217,0.00001815888,0.00001347768,0.00001510051,0.00006139572,0.9688985,0.005006394,0.0003035703,0.02305281,0.0001177579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05451838,0.000411835,0.937158,0.0006775229,0.0004095804,0.00005131863,6.513576e-7,0.0002092036,0.006563538],"genre_scores_gemma":[0.9715887,0.00005064419,0.02683335,0.0002835141,0.0002024105,0.00000259733,0.000009116642,0.000008885019,0.001020773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9170703,"threshold_uncertainty_score":0.3263026,"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."}}