{"id":"W3012513693","doi":"10.5220/0008894100170025","title":"Detecting Bidding Fraud using a Few Labeled Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Bidding; Computer science; Business; Marketing","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":[0.002455272,0.0008454472,0.001124147,0.003682814,0.001070779,0.002213402,0.001237119,0.001939189,0.001356698],"category_scores_gemma":[0.01003832,0.0004623704,0.0007467804,0.002378738,0.0006367184,0.003056243,0.0008696968,0.001574915,0.001071894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008713254,"about_ca_system_score_gemma":0.001305954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003518792,"about_ca_topic_score_gemma":0.006343393,"domain_scores_codex":[0.9973142,0.0006847431,0.0002547454,0.0004638569,0.001017967,0.0002644955],"domain_scores_gemma":[0.9880269,0.006258797,0.001045455,0.001671601,0.002569744,0.0004275496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.006093041,0.006009731,0.3252449,0.001036432,0.0009516941,0.002457985,0.0005898916,0.1062623,0.04309573,0.009944909,0.0289408,0.4693727],"study_design_scores_gemma":[0.0002070554,0.0007209604,0.06869238,0.000165465,0.0003845621,0.001126529,0.001213497,0.862345,0.03325979,0.01975686,0.01194835,0.0001796431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.87842,0.00107635,0.1039882,0.001350028,0.0002727275,0.0002860824,0.005872155,0.0009074183,0.007827118],"genre_scores_gemma":[0.9348156,0.0002066415,0.05553935,0.0002162617,0.00009897177,0.0001169475,0.007501821,0.00003936592,0.001465015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003682814,"threshold_uncertainty_score":0.01298493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2884773521927131,"score_gpt":0.3177294045921281,"score_spread":0.02925205239941497,"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."}}