{"id":"W4383768032","doi":"10.53829/ntr201906fa10","title":"Per-device Policy Control Technology Using Artificial Intelligence","year":2019,"lang":"en","type":"article","venue":"NTT technical review","topic":"Access Control and Trust","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Future Earth","funders":"","keywords":"Control (management); Computer science; Artificial intelligence; Engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001088376,0.0001609043,0.0005427896,0.0001426294,0.0002117504,0.00005357326,0.000766954,0.0002623331,0.001000414],"category_scores_gemma":[0.001856889,0.0001353151,0.0001724948,0.001209327,0.0003578617,0.0001847588,0.0001080483,0.0003758769,0.001241097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001699274,"about_ca_system_score_gemma":0.0003894592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004681516,"about_ca_topic_score_gemma":0.0002197887,"domain_scores_codex":[0.9980665,0.0001702649,0.0005029881,0.0003596864,0.000400419,0.0005000965],"domain_scores_gemma":[0.9988831,0.0002040465,0.0001793753,0.0004302571,0.0001694791,0.0001337721],"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":[0.000005286591,0.00004352787,0.0003041695,0.0001503104,0.000009126555,0.000003170152,0.0000132813,0.000003516414,0.0004502469,0.6901996,0.00005981513,0.3087579],"study_design_scores_gemma":[0.0002165934,0.0001456878,0.0002676751,0.002815935,0.0002202285,0.00002253557,0.0001938452,0.0004173955,0.0001062927,0.1356023,0.8592913,0.0007002582],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.007783474,0.3740835,0.08827372,0.2597272,0.001326262,0.01017103,0.00003702615,0.002960641,0.2556371],"genre_scores_gemma":[0.979606,0.01519853,0.001116859,0.003566336,0.0003172372,0.00004966538,9.315547e-7,0.00001563596,0.0001288212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9718225,"threshold_uncertainty_score":0.9999128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06467127815383024,"score_gpt":0.4210771714093762,"score_spread":0.3564058932555459,"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."}}