{"id":"W4313343372","doi":"10.1007/978-3-031-22295-5_19","title":"VinciDecoder: Automatically Interpreting Provenance Graphs into Textual Forensic Reports with Application to OpenStack","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Cloud computing; Provenance; Sentence; Tree (set theory); Testbed; Data science; Natural language processing; Artificial intelligence; World Wide Web; Information retrieval","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"],"consensus_categories":[],"category_scores_codex":[0.0008149367,0.000602525,0.0005986045,0.0005963937,0.0003622977,0.0008874152,0.002929783,0.0001517432,0.00001443068],"category_scores_gemma":[0.0001199606,0.0005062025,0.0001241234,0.001154291,0.000641126,0.001086733,0.003227726,0.0007023967,0.00002795288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004581765,"about_ca_system_score_gemma":0.0007278953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006237943,"about_ca_topic_score_gemma":0.0002944801,"domain_scores_codex":[0.9948057,0.00002535725,0.0008000737,0.002180178,0.001472242,0.0007164421],"domain_scores_gemma":[0.9966441,0.0002514601,0.0005041553,0.001988111,0.0003245725,0.0002876397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001043888,0.00003325312,0.0000793781,0.00003688998,0.00001290257,0.0002724931,0.001183493,0.01646826,0.00005950982,0.1525881,0.00007063579,0.8291847],"study_design_scores_gemma":[0.0002207189,0.001031144,0.0001391433,0.0008034016,0.00001517237,0.000891179,0.00000168572,0.3091887,0.001173825,0.6741808,0.01083178,0.001522391],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00040625,0.0000636269,0.9824191,0.001114141,0.0006250625,0.001008119,0.000004756695,0.0003063101,0.01405269],"genre_scores_gemma":[0.41932,0.000003949195,0.5765449,0.003599789,0.00009745632,0.00010742,0.00001603806,0.00005658517,0.0002538541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8276623,"threshold_uncertainty_score":0.9997389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005546182153218648,"score_gpt":0.2194010435745475,"score_spread":0.2138548614213288,"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."}}