{"id":"W2119027445","doi":"10.1109/re.2009.41","title":"Finding Defects in Natural Language Confidentiality Requirements","year":2009,"lang":"en","type":"article","venue":"","topic":"Information and Cyber Security","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Confidentiality; Traceability; Natural language; Annotation; Normalization (sociology); Requirements analysis; Natural language processing; Requirements traceability; Information retrieval; Software requirements; Requirements engineering; Software; Software engineering; Artificial intelligence; Software development; Requirement; Programming language; Software design; Computer security","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":[0.004439724,0.0005624137,0.0004599192,0.003375085,0.0007373818,0.001275527,0.001263408,0.001733784,0.001145069],"category_scores_gemma":[0.05109557,0.0004820307,0.0007001258,0.001752462,0.00127823,0.003216682,0.001226655,0.001215217,0.0002972125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001260836,"about_ca_system_score_gemma":0.002027908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004757552,"about_ca_topic_score_gemma":0.005643163,"domain_scores_codex":[0.993591,0.001733581,0.0005222851,0.0008067142,0.003062322,0.0002841251],"domain_scores_gemma":[0.9187834,0.05526542,0.01054465,0.00576682,0.009246471,0.0003933501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008648596,0.001361124,0.2108192,0.002808696,0.00021465,0.01601843,0.02172044,0.08447297,0.08532169,0.0532338,0.01553989,0.5076242],"study_design_scores_gemma":[0.0002058504,0.0006470798,0.08040217,0.0005977911,0.0003261439,0.009605994,0.01458899,0.6320208,0.1635089,0.06621535,0.03159802,0.0002829995],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7354358,0.0001979101,0.2558077,0.0009762652,0.00005038322,0.0003639315,0.0008570064,0.00343749,0.002873502],"genre_scores_gemma":[0.8025405,0.0001154568,0.1938889,0.0001575789,0.00001667121,0.0001905656,0.001398223,0.0005285862,0.001163488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004757552,"threshold_uncertainty_score":0.02347982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01590073312083285,"score_gpt":0.2883572313396057,"score_spread":0.2724564982187729,"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."}}