{"id":"W3103349515","doi":"10.6084/m9.figshare.8281412.v1","title":"BUILDING HIGH-QUALITY AUCTION FRAUD DATASET","year":2019,"lang":"en","type":"article","venue":"Figshare","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Unavailability; Bidding; Quality (philosophy); Common value auction; USable; Computer security; Paillier cryptosystem; Artificial intelligence; World Wide Web; Business; Reliability 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001119121,0.00009161668,0.0001019416,0.00006308944,0.0000542751,0.0001502474,0.001032945,0.00006677501,0.02113453],"category_scores_gemma":[0.0002758738,0.00009203661,0.00002309699,0.000247192,0.000003030036,0.001002195,0.0004003511,0.0001224598,0.007267036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000522272,"about_ca_system_score_gemma":0.0000343681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001453162,"about_ca_topic_score_gemma":9.259153e-7,"domain_scores_codex":[0.9990017,0.0000501092,0.000172294,0.0003823759,0.0002235923,0.0001698629],"domain_scores_gemma":[0.9984605,0.00007907701,0.0001313071,0.001215101,0.00006679292,0.00004723302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[9.578384e-7,0.00001372874,0.00001998201,0.0000409505,0.000002936407,0.000001093018,0.000010587,0.000007289885,0.002602723,0.01511859,0.9759343,0.006246877],"study_design_scores_gemma":[0.0001207216,0.00002167086,0.009294325,0.0001709051,8.289017e-7,0.000004489872,0.000002141459,0.001899538,0.03588187,0.002148362,0.9502447,0.000210443],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004341273,0.00003310592,0.08792498,0.0005482791,0.0003028724,0.0004662348,0.9074258,0.001288741,0.001575874],"genre_scores_gemma":[0.1241028,0.000001522351,0.1245002,0.001702755,0.0001465154,0.0001877014,0.7490649,0.00001938474,0.0002741796],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1583608,"threshold_uncertainty_score":0.9935059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05053754155162595,"score_gpt":0.3279106271843663,"score_spread":0.2773730856327404,"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."}}