{"id":"W4392158122","doi":"10.1109/globecom54140.2023.10437829","title":"Strengthening Open Radio Access Networks: Advancing Safeguards Through ZTA and Deep Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Radio frequency; Computer network; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005148504,0.000191709,0.0002727838,0.00008595104,0.0006711733,0.001209763,0.001525873,0.00006259541,0.0000671066],"category_scores_gemma":[0.0001258775,0.0001712992,0.00005993814,0.001309077,0.00004753444,0.003464191,0.002904711,0.0003512565,0.00005358061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004740364,"about_ca_system_score_gemma":0.00005221814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002959449,"about_ca_topic_score_gemma":0.0001239627,"domain_scores_codex":[0.9982135,0.0001194924,0.0002456087,0.0006131789,0.000248952,0.0005592588],"domain_scores_gemma":[0.9989141,0.0003391614,0.00008472128,0.0004463757,0.00007378168,0.0001419171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004148794,0.0001342729,0.005429777,0.00006396391,0.0001412546,0.00008219169,0.004062395,0.1183177,0.0001902983,0.359198,0.009829869,0.5025088],"study_design_scores_gemma":[0.0005617633,0.0001092333,0.004436291,0.00005434414,0.0000137514,0.00001382588,0.0003287027,0.9060996,0.000174647,0.02332432,0.06446451,0.0004189726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01549051,0.0002682361,0.9566404,0.0008521186,0.0005696439,0.0002755214,0.000001105539,0.0009298885,0.02497264],"genre_scores_gemma":[0.9792497,0.0004537193,0.01766583,0.0004735543,0.0003408709,0.00004128642,0.00001706756,0.00002571721,0.001732259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9637592,"threshold_uncertainty_score":0.9998271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02368813124934608,"score_gpt":0.298625614585494,"score_spread":0.2749374833361479,"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."}}