{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000326098,0.00006462076,0.00006761937,0.000032198,0.0001393901,0.000106726,0.0004285347,0.00004473522,0.00002871334],"category_scores_gemma":[0.00002158592,0.00005099299,0.00005413159,0.00005824538,0.00001246744,0.0006771635,0.00009447935,0.000122976,0.00004071589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001107229,"about_ca_system_score_gemma":0.00006789137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002329562,"about_ca_topic_score_gemma":0.00005652356,"domain_scores_codex":[0.9994342,0.000008995778,0.0001475889,0.0001278209,0.0001178813,0.0001634794],"domain_scores_gemma":[0.9993885,0.00004026433,0.00005271103,0.000361088,0.00008757967,0.00006983701],"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":[7.340632e-7,0.00002525199,0.00007445229,0.000001718149,0.000002875791,5.640939e-8,0.001113022,0.0001012432,0.00007781992,0.9679973,0.009203722,0.02140188],"study_design_scores_gemma":[0.0002015842,0.00001209556,0.0003976642,5.478119e-7,7.588833e-7,9.087879e-7,0.00002260208,0.9507033,0.0003725329,0.03275779,0.01544682,0.00008336656],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003199303,0.000003047223,0.9153717,0.001211063,0.0004073482,0.0001292241,0.000002570602,0.000141289,0.07953444],"genre_scores_gemma":[0.5080128,0.000003707438,0.4891426,0.001883108,0.00003698102,0.00003582617,0.000001880243,0.00000218561,0.0008809128],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9506021,"threshold_uncertainty_score":0.2079434,"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."}}