{"id":"W1964636354","doi":"10.1109/pst.2014.6890960","title":"Validating trust models against realworld data sets","year":2014,"lang":"en","type":"article","venue":"","topic":"Access Control and Trust","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ground truth; Profit (economics); Order (exchange); Data modeling; Process (computing); Empirical research; Data mining; Data science; Machine learning; Database; Business","routes":{"ca_aff":true,"ca_fund":true,"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":[0.03957253,0.00119393,0.0007022052,0.0033359,0.001384148,0.002733862,0.00259719,0.002307929,0.001715433],"category_scores_gemma":[0.185338,0.0005604211,0.001651362,0.003579214,0.002354601,0.005396711,0.002123042,0.00302148,0.0006116122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005387932,"about_ca_system_score_gemma":0.002135575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219644,"about_ca_topic_score_gemma":0.01698038,"domain_scores_codex":[0.9601169,0.02978539,0.002722446,0.003254152,0.003411148,0.0007101413],"domain_scores_gemma":[0.7370811,0.2040261,0.01115897,0.03064925,0.01543161,0.001652973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00115863,0.002356753,0.1503468,0.001175271,0.001359992,0.0005398436,0.003330132,0.7385347,0.002413194,0.03934844,0.01671379,0.04272243],"study_design_scores_gemma":[0.0001331831,0.0005112235,0.01635778,0.0001976313,0.00009906809,0.0002264011,0.001007044,0.9564723,0.003387778,0.01598753,0.005548884,0.00007125869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8367549,0.0005205956,0.1403721,0.003141542,0.0002609671,0.001065657,0.01045381,0.001055884,0.006374722],"genre_scores_gemma":[0.8945065,0.0001707131,0.09408476,0.0002603235,0.00002985769,0.0005298723,0.009709575,0.00008158767,0.0006268928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03957253,"threshold_uncertainty_score":0.209282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1126299141058683,"score_gpt":0.3579734815684081,"score_spread":0.2453435674625398,"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."}}