{"id":"W4403222415","doi":"10.2139/ssrn.4980325","title":"Automatic Instantiation of Assurance Cases from Patterns Using Large Language Models","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Safety Systems Engineering in Autonomy","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; York University","funders":"","keywords":"Computer science; Programming language; Software engineering; Linguistics; Natural language processing; Philosophy","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":[0.003392461,0.001498354,0.001201491,0.002318541,0.0007763337,0.003295093,0.002126295,0.002037533,0.005807172],"category_scores_gemma":[0.02539073,0.001908344,0.003190384,0.001394217,0.001518515,0.005415575,0.00540388,0.002890733,0.00180729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000805525,"about_ca_system_score_gemma":0.001522473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001740629,"about_ca_topic_score_gemma":0.00344718,"domain_scores_codex":[0.9946838,0.001749368,0.0005197876,0.0007932329,0.001909786,0.00034406],"domain_scores_gemma":[0.9819078,0.0116669,0.0009614077,0.003950192,0.001120523,0.0003932657],"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":[0.001262195,0.001055709,0.02156276,0.002005656,0.0005102634,0.006163654,0.00403605,0.1261361,0.06742607,0.1156418,0.02334268,0.6308571],"study_design_scores_gemma":[0.0001760867,0.0001286424,0.001399705,0.0002179658,0.0002208978,0.001045589,0.0003986461,0.8413565,0.03590688,0.09505332,0.02398986,0.0001060082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03707927,0.0001481209,0.9321544,0.0004128175,0.00005886451,0.0004595252,0.0009181909,0.0258023,0.002966568],"genre_scores_gemma":[0.3350736,0.000184322,0.6553366,0.0002073853,0.00004735532,0.0003263839,0.002924866,0.003820619,0.002078854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005807172,"threshold_uncertainty_score":0.01942688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01105182713125467,"score_gpt":0.2358639729466211,"score_spread":0.2248121458153665,"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."}}