{"id":"W1899838367","doi":"10.1002/sec.413","title":"A secure, efficient, and cost‐effective distributed architecture for spam mitigation on LTE 4G mobile networks","year":2012,"lang":"en","type":"article","venue":"Security and Communication Networks","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Computer science; Computer network; Dimensioning; Flooding (psychology); Architecture; Distributed computing; Network packet; Network architecture; Cellular network; Computer security","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":[0.0008553205,0.0002233863,0.0002652878,0.0000712075,0.0004999017,0.0002123255,0.0004785379,0.0001916167,0.000003460659],"category_scores_gemma":[0.00004055593,0.0002040996,0.00009936016,0.0003079824,0.0001301252,0.0001988284,0.0002906431,0.0005133277,0.000001376138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004252073,"about_ca_system_score_gemma":0.000009424976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001024894,"about_ca_topic_score_gemma":0.00006014499,"domain_scores_codex":[0.9984626,0.000342531,0.0002856964,0.0003378359,0.0001494896,0.0004218736],"domain_scores_gemma":[0.9983133,0.0007189075,0.0002048082,0.0004785052,0.000117179,0.0001673033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000749805,0.0002945483,0.0003861255,0.0000323763,0.0001226054,4.769154e-7,0.006195574,0.5270543,0.000002212282,0.3954261,0.001006415,0.06940432],"study_design_scores_gemma":[0.0004607053,0.0001220505,0.0005646091,0.0000885883,0.00003985207,0.000008808687,0.0001551357,0.9906897,0.00001157449,0.0003962009,0.00723837,0.0002243995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06171716,0.005513687,0.9307948,0.0003102346,0.00015931,0.00120685,0.00001390757,0.0001122486,0.000171782],"genre_scores_gemma":[0.997296,0.0003139497,0.001223806,0.0003604248,0.0001811619,0.0003878147,0.0002152102,0.00001332324,0.000008291646],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9355789,"threshold_uncertainty_score":0.8322939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006695986757950451,"score_gpt":0.2402966456008903,"score_spread":0.2336006588429398,"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."}}