{"id":"W4402571103","doi":"10.1109/icstw60967.2024.00017","title":"Machine Learning for Cross-Vulnerability Prediction in Smart Contracts","year":2024,"lang":"en","type":"article","venue":"","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Vulnerability (computing); Computer science; 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":[0.001029515,0.0000900955,0.0001961918,0.000165769,0.00008090414,0.0001323155,0.00007261276,0.00006982178,0.0002818287],"category_scores_gemma":[0.0001784815,0.00009758793,0.0000879065,0.0001869767,0.00002345927,0.0002915203,0.00002087311,0.000175164,0.0003416836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001052233,"about_ca_system_score_gemma":0.000009265429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005447232,"about_ca_topic_score_gemma":0.0001916712,"domain_scores_codex":[0.9989651,0.000006638266,0.0004427025,0.0003423889,0.00002043211,0.0002227542],"domain_scores_gemma":[0.9997231,0.00006610445,0.00005081187,0.0001187568,0.00001614157,0.00002505789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003710356,0.00005706178,0.541068,0.00009674131,0.00001615322,0.000003706019,0.0001639625,0.001389168,0.000005858485,0.4445684,0.000544122,0.01204972],"study_design_scores_gemma":[0.0004625296,0.0000746746,0.3492619,0.00001622801,0.000002036061,4.865674e-7,0.0000096065,0.1217889,0.00002807449,0.04535544,0.4828746,0.0001255308],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6606404,0.008088979,0.1080969,0.001144348,0.003169402,0.001322152,0.0003779924,0.0003683385,0.2167914],"genre_scores_gemma":[0.9896753,0.0001901584,0.0002430138,0.0001047702,0.0001133125,0.0001008405,0.00003641754,0.00001510964,0.009521084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4823305,"threshold_uncertainty_score":0.4391766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02740221320241155,"score_gpt":0.2576852032446956,"score_spread":0.2302829900422841,"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."}}