{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002183991,0.00005259519,0.00005991957,0.00007034062,0.00003877496,0.0001161318,0.0003028126,0.0000234425,0.00003965078],"category_scores_gemma":[0.00001595216,0.00004688884,0.00002544193,0.0001934262,0.000005849283,0.0007995592,0.00005003343,0.00008118698,0.00008079803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003117372,"about_ca_system_score_gemma":0.00001554329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000730584,"about_ca_topic_score_gemma":0.0000998935,"domain_scores_codex":[0.9994143,0.00002517213,0.0001447356,0.0001014482,0.0001601208,0.000154267],"domain_scores_gemma":[0.9997248,0.000009624756,0.00003368598,0.0001842506,0.00001768008,0.00002993432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00000759521,0.0001059262,0.002835938,0.00001335253,0.000004723089,0.00003867227,0.02021902,0.00001125479,0.002670306,0.8524605,0.002132767,0.1195],"study_design_scores_gemma":[0.004305266,0.0001903285,0.7356142,0.0001063436,0.000006330054,0.00004826294,0.001850581,0.1681385,0.06568172,0.01838076,0.004464543,0.0012132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7739845,0.000071516,0.05089914,0.00063081,0.0005478899,0.000161536,3.560715e-7,0.0002308192,0.1734734],"genre_scores_gemma":[0.9962295,0.000001155701,0.001530034,0.001969804,0.00001210727,8.310043e-7,0.000002069045,6.855628e-7,0.0002537908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8340797,"threshold_uncertainty_score":0.1912071,"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."}}