{"id":"W2969695177","doi":"10.1109/est.2019.8806210","title":"Risk Inference Models for Security Applications","year":2019,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Inference; Bayesian network; Machine learning; Frequentist inference; Probabilistic logic; Artificial intelligence; Data mining; Inference engine; Fiducial inference; Bayesian inference; Metric (unit); Adaptive neuro fuzzy inference system; Bayesian probability; Fuzzy logic; Fuzzy control system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001902144,0.00008644447,0.00009958721,0.0000366997,0.00008085866,0.0001074162,0.0006665044,0.00005301154,0.00001840605],"category_scores_gemma":[0.00001170567,0.00007577887,0.0000496346,0.0001582603,0.00001364912,0.0004223249,0.0001149682,0.00009641577,0.0002101201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001264969,"about_ca_system_score_gemma":0.00006283948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004320748,"about_ca_topic_score_gemma":0.000007483416,"domain_scores_codex":[0.9992059,0.00001777631,0.0001376493,0.0003358395,0.0001109398,0.0001919374],"domain_scores_gemma":[0.9990284,0.0001400266,0.00005240489,0.0005819181,0.0001259673,0.00007124926],"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":[0.0000010262,0.00002292121,0.0001888492,0.000007952083,0.000003846635,2.892034e-8,0.0001053145,0.003609037,0.00004284219,0.9796009,0.0001957303,0.01622156],"study_design_scores_gemma":[0.00007718099,0.00002104423,0.00002284831,0.000002405762,0.000001678841,3.877187e-7,0.00000529281,0.6016834,0.0001766755,0.3968259,0.001107002,0.00007622154],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002523881,0.00003481142,0.9823344,0.0002151947,0.00007030709,0.0003922132,0.000008340213,0.0002287144,0.01419207],"genre_scores_gemma":[0.8666737,0.00002086609,0.132277,0.0002235018,0.0000218876,0.0001400535,0.000002475143,0.000004148863,0.0006363785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8641498,"threshold_uncertainty_score":0.3090173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02282365219403852,"score_gpt":0.2771049164665415,"score_spread":0.2542812642725029,"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."}}