{"id":"W4388625507","doi":"10.1145/3626111.3628182","title":"Harnessing ML For Network Protocol Assessment","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Protocol (science); Process (computing); Network congestion; Distributed computing; Communications protocol; Network simulation; Machine learning; Artificial intelligence; Computer network; Operating system","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":[0.009215032,0.001489903,0.001097419,0.002548174,0.000550582,0.003615402,0.003093872,0.001720914,0.004092466],"category_scores_gemma":[0.0642273,0.0007477867,0.0006923652,0.0008421041,0.002233149,0.007773398,0.004207375,0.003987351,0.00151583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001242024,"about_ca_system_score_gemma":0.001556197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008059738,"about_ca_topic_score_gemma":0.0007245371,"domain_scores_codex":[0.9915321,0.004012489,0.0004643675,0.0009720045,0.002698081,0.0003209382],"domain_scores_gemma":[0.9581391,0.02871107,0.002543994,0.00717437,0.002939686,0.0004918654],"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":[0.0004232981,0.0004103782,0.007605749,0.000419203,0.0002435023,0.0002291451,0.0004412655,0.4670103,0.0155274,0.1229685,0.003709356,0.3810119],"study_design_scores_gemma":[0.0000173618,0.00008931036,0.0002486742,0.0000360637,0.00001670901,0.0000544435,0.00003206532,0.9214302,0.009176995,0.06643565,0.002434412,0.00002807931],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008589259,0.0001752931,0.9824011,0.0003644412,0.00005677469,0.00007796105,0.00006984492,0.005695705,0.002569678],"genre_scores_gemma":[0.6569241,0.00028087,0.338947,0.0003659806,0.0001102666,0.0002748297,0.0002230737,0.0007655236,0.002108267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009215032,"threshold_uncertainty_score":0.04873431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02648761047414976,"score_gpt":0.367661214310515,"score_spread":0.3411736038363652,"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."}}