{"id":"W2020974462","doi":"10.1109/icimp.2010.15","title":"FEMRA: Fuzzy Expert Model for Risk Assessment","year":2010,"lang":"en","type":"article","venue":"","topic":"Information and Cyber Security","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Risk assessment; Risk analysis (engineering); NIST; Risk management; Computer science; Fuzzy logic; Context (archaeology); Process (computing); IT risk management; Knowledge management; Management science; Computer security; Engineering; Artificial intelligence; 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":[0.002647526,0.0009194714,0.0009960885,0.001525739,0.0007329886,0.002055204,0.002242157,0.002548532,0.007607057],"category_scores_gemma":[0.006335357,0.0004469825,0.001377488,0.001149723,0.0007377509,0.002176754,0.0008630793,0.001695116,0.001546412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001686067,"about_ca_system_score_gemma":0.001662281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01272605,"about_ca_topic_score_gemma":0.008242328,"domain_scores_codex":[0.9981079,0.0008528816,0.00009942657,0.0002860877,0.0005264192,0.0001272195],"domain_scores_gemma":[0.9975309,0.00147708,0.0001704311,0.0001155782,0.0006257267,0.00008040848],"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":[0.00009277004,0.00006103534,0.0008263908,0.0001247103,0.00009159266,0.0002529956,0.0002217992,0.8446939,0.00105396,0.09287322,0.00421869,0.05548896],"study_design_scores_gemma":[0.000009709998,0.00001983772,0.00009011521,0.00001677433,0.00001111646,0.00006241497,0.00001734885,0.9718973,0.0001368933,0.02536278,0.002362914,0.00001274574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006151223,0.0004033884,0.9830884,0.0005089862,0.00006734839,0.00008282326,0.0002594805,0.0003468026,0.009091564],"genre_scores_gemma":[0.4614872,0.001019832,0.5157118,0.0003784767,0.0002269635,0.0004925233,0.0007343725,0.0001029579,0.01984577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01272605,"threshold_uncertainty_score":0.02544808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01781948881736326,"score_gpt":0.2936014527817035,"score_spread":0.2757819639643402,"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."}}