{"id":"W6968402249","doi":"10.5281/zenodo.14550793","title":"Silhouette: Leveraging Consistency Mechanisms to Detect Bugs in Persistent Memory-Based File Systems","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Usability and User Interface Design","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Consistency (knowledge bases); Artifact (error); Data consistency; Causal consistency; Strong consistency; Data integrity; File system","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008815338,0.0003064089,0.0003324493,0.0008754095,0.0004155394,0.001737012,0.002406858,0.0001744817,0.01942603],"category_scores_gemma":[0.0003650615,0.0003257337,0.0001341719,0.000958015,0.00007537987,0.0001321597,0.0014223,0.0004726275,0.02926566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003770506,"about_ca_system_score_gemma":0.00001868854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001738239,"about_ca_topic_score_gemma":0.000004077361,"domain_scores_codex":[0.9969922,0.0005358816,0.000364724,0.0010201,0.0005745481,0.0005125311],"domain_scores_gemma":[0.9982966,0.0000459875,0.0001180421,0.001089873,0.000218922,0.0002306136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001233446,0.00005428533,4.036007e-8,0.0003962221,0.00005240362,0.00006672362,0.0009925272,0.0004671694,0.0005936745,0.001765284,0.9854989,0.01010045],"study_design_scores_gemma":[0.0002428783,0.0003191377,0.00000224324,0.0006708583,0.00001740235,0.00005310297,0.000241892,0.007895454,0.0002266746,0.0001939072,0.9897586,0.0003778447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0000427651,0.0008346998,0.3149997,0.001675163,0.001121503,0.001960067,0.001072032,0.004066465,0.6742277],"genre_scores_gemma":[0.07884692,0.00006573745,0.03216106,0.002288732,0.0007096619,0.000004209011,0.002113846,0.02572327,0.8580866],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2828386,"threshold_uncertainty_score":0.9999195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04361520474334294,"score_gpt":0.2399448062522263,"score_spread":0.1963296015088834,"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."}}