{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003105477,0.0003573351,0.0003533601,0.0005773868,0.0006279798,0.001132999,0.001521553,0.0003150874,0.00002096004],"category_scores_gemma":[0.0008123685,0.0003579375,0.0002096064,0.0004327423,0.0001844606,0.003678446,0.000422935,0.0004263859,0.00005226422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004254464,"about_ca_system_score_gemma":0.0008115583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003343933,"about_ca_topic_score_gemma":0.0001177464,"domain_scores_codex":[0.9968005,0.00004088092,0.0008652693,0.0007331177,0.001062819,0.0004974256],"domain_scores_gemma":[0.9962923,0.00104419,0.0005248222,0.0009032264,0.001144506,0.00009094363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001776036,0.00002034096,0.000009631909,0.00007827903,0.00001489219,4.812821e-7,0.003816186,0.02570992,0.00006126748,0.03717167,0.00011813,0.9329814],"study_design_scores_gemma":[0.000464164,0.00005711041,0.0001449674,0.0001005726,0.00001645387,0.000003015522,0.000001222358,0.7650508,0.0005107182,0.2310178,0.002298175,0.0003349457],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002908852,0.00002781782,0.9921457,0.0006969583,0.003820339,0.002065057,0.000110832,0.000239668,0.0006027818],"genre_scores_gemma":[0.2046214,0.00001369634,0.7908579,0.002086621,0.001112819,0.0005815654,0.0005548954,0.00004017941,0.000130861],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9326465,"threshold_uncertainty_score":0.9999039,"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."}}