{"id":"W7125999546","doi":"10.1109/ase63991.2025.00394","title":"Detecting Vulnerabilities from Issue Reports for Internet-of-Things","year":2025,"lang":"","type":"article","venue":"","topic":"Information and Cyber Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Eclipse; Identification (biology); Set (abstract data type); Software; Natural language; Support vector machine; Secure coding","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":[0.002354653,0.00107782,0.0004032505,0.004867224,0.0004534795,0.001263263,0.000612037,0.0008689355,0.0009124676],"category_scores_gemma":[0.02347468,0.0002714671,0.0007158117,0.002209309,0.0003488686,0.003023533,0.001458135,0.001092761,0.001106118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004163744,"about_ca_system_score_gemma":0.0008554925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002542964,"about_ca_topic_score_gemma":0.003835516,"domain_scores_codex":[0.9971538,0.0007558175,0.0003892285,0.0004150862,0.001125395,0.0001607627],"domain_scores_gemma":[0.9835903,0.007253051,0.00396657,0.002312904,0.002525783,0.0003514057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005604051,0.0006013817,0.2707449,0.002072243,0.0003306061,0.003381727,0.002756743,0.0237163,0.04910273,0.003924594,0.06426727,0.578541],"study_design_scores_gemma":[0.00007082042,0.000727319,0.2448125,0.0007072805,0.0003723575,0.004681997,0.003652319,0.5110207,0.120682,0.01992015,0.09311635,0.0002363643],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.794697,0.002522113,0.1251944,0.003677767,0.0006352311,0.0005923575,0.01658326,0.04598143,0.01011637],"genre_scores_gemma":[0.8868447,0.0008827212,0.08518878,0.0004454429,0.0001747037,0.0001902743,0.02245403,0.001158565,0.002660783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004867224,"threshold_uncertainty_score":0.01245272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227774312956058,"score_gpt":0.2610820707319268,"score_spread":0.2488043276023662,"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."}}