{"id":"W1491103366","doi":"","title":"Fixing races for fun and profit: how to use access(2)","year":2004,"lang":"en","type":"article","venue":"","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Executor; Computer science; Probabilistic logic; Computer security; Artificial intelligence; Economics; Finance","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":[0.007606118,0.000628405,0.0009268761,0.0006671539,0.002446213,0.00343908,0.002261043,0.002496254,0.008136746],"category_scores_gemma":[0.04845902,0.0008998553,0.001554818,0.0006007433,0.007910221,0.01638337,0.006756308,0.00363528,0.0013793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533185,"about_ca_system_score_gemma":0.002929447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003176663,"about_ca_topic_score_gemma":0.002736988,"domain_scores_codex":[0.9923434,0.003012514,0.0003604182,0.001297092,0.001515736,0.001470839],"domain_scores_gemma":[0.9716106,0.01400184,0.001903124,0.0101082,0.001501023,0.0008753067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00150499,0.0002703851,0.007556447,0.0002689309,0.0001711193,0.0005880388,0.002852125,0.04325966,0.01733159,0.7489696,0.01457432,0.1626529],"study_design_scores_gemma":[0.0001622779,0.0001911037,0.001095032,0.0001220018,0.0001458765,0.0004089019,0.0005613278,0.1456336,0.03601357,0.79067,0.02483462,0.0001616072],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1850642,0.0004535875,0.7754369,0.007774999,0.0003383769,0.0003129567,0.0002047806,0.00658068,0.02383351],"genre_scores_gemma":[0.8559534,0.0001809017,0.1333148,0.0006316977,0.0000934184,0.000248724,0.00008815329,0.00160453,0.007884238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008136746,"threshold_uncertainty_score":0.04022551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08609299613582785,"score_gpt":0.3199793802399711,"score_spread":0.2338863841041432,"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."}}