{"id":"W2492668260","doi":"10.12694/scpe.v17i3.1180","title":"Analysis and Verification of XACML Policies in a Medical Cloud Environment","year":2016,"lang":"en","type":"article","venue":"Scalable Computing Practice and Experience","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Bundesministerium für Bildung und Forschung; Deutscher Akademischer Austauschdienst","keywords":"XACML; Cloud computing; Computer science; Computer security; Operating system; Authorization","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":[0.0005585154,0.00009268813,0.000167889,0.0001863606,0.00008425518,0.00008117159,0.0001645439,0.00005378848,0.0001441772],"category_scores_gemma":[0.0008161319,0.00006703081,0.00001843158,0.0005621786,0.0001928719,0.001087789,0.0002628536,0.00006080223,0.00001694986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000011083,"about_ca_system_score_gemma":0.00001147986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001362501,"about_ca_topic_score_gemma":0.00003848504,"domain_scores_codex":[0.9990047,0.00001827588,0.0002643215,0.0002743212,0.0002667717,0.0001715962],"domain_scores_gemma":[0.9992958,0.0002410763,0.0002010683,0.0001946393,0.00004953801,0.00001792264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001702417,0.0003458853,0.6337632,0.0002115154,0.0001005319,0.00001936676,0.002846953,0.000103173,0.005206679,0.01580544,0.0002251294,0.3412019],"study_design_scores_gemma":[0.001495234,0.00006188932,0.7107704,0.0008079936,0.0004869268,0.00005349546,0.01029101,0.07289649,0.004462434,0.00120773,0.1964057,0.001060627],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774317,0.0002831732,0.01835805,0.002967394,0.00008662881,0.00006494473,9.356177e-7,0.00001803359,0.0007891454],"genre_scores_gemma":[0.9984227,0.0004908109,0.0004753722,0.0004472542,0.0001300119,0.000003727824,0.000002498744,0.000004501115,0.00002314981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3401412,"threshold_uncertainty_score":0.2733437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03458922695781805,"score_gpt":0.3084640098878952,"score_spread":0.2738747829300771,"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."}}