{"id":"W4383900947","doi":"10.1007/978-3-031-37586-6_23","title":"VIET: A Tool for Extracting Essential Information from Vulnerability Descriptions for CVSS Evaluation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Information and Cyber Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Vulnerability (computing); Computer science; Vulnerability assessment; Exploit; Process (computing); Artificial intelligence; Machine learning; Computer security; Data mining","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":[0.00151666,0.001641113,0.0005808915,0.007508349,0.0006300954,0.002286709,0.001167267,0.0008882047,0.02068222],"category_scores_gemma":[0.009013194,0.0008283511,0.001131263,0.002605436,0.000445093,0.003034367,0.001545296,0.0009167329,0.007622393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008249588,"about_ca_system_score_gemma":0.001976658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004411705,"about_ca_topic_score_gemma":0.005757333,"domain_scores_codex":[0.9988815,0.0002019399,0.000165375,0.0001440415,0.0005443981,0.00006274629],"domain_scores_gemma":[0.9954336,0.002313389,0.0004710052,0.0007377822,0.0009392215,0.0001049123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004020025,0.0002466827,0.01069015,0.002671248,0.000290798,0.001196455,0.001536664,0.01641936,0.02511091,0.0199376,0.1760901,0.7454079],"study_design_scores_gemma":[0.0002109367,0.0004273715,0.01329998,0.001459807,0.0005199536,0.003279177,0.00152347,0.3183553,0.1051724,0.05481504,0.5005553,0.0003812677],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01910914,0.0004682589,0.707833,0.0001985278,0.00008824947,0.0007485797,0.03396494,0.2214256,0.01616376],"genre_scores_gemma":[0.1180972,0.0006376505,0.7792364,0.0001981487,0.00005688045,0.0008884221,0.07134624,0.01619278,0.01334625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02068222,"threshold_uncertainty_score":0.06918889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03778039143954876,"score_gpt":0.2976723096424599,"score_spread":0.2598919182029111,"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."}}