{"id":"W2983320559","doi":"10.1109/ancs.2019.8901887","title":"Perfect is the Enemy of Good: Lloyd-Max Quantization for Rate Allocation in Congestion Control Plane","year":2019,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Quantization (signal processing); Computer science; Mathematical optimization; Decoupling (probability); Mathematics; Algorithm; 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":[],"consensus_categories":[],"category_scores_codex":[0.0007786564,0.0001047013,0.0001807455,0.00006771788,0.00004577026,0.00005162119,0.0003407615,0.00006633438,0.00003852299],"category_scores_gemma":[0.00004305702,0.00007494429,0.00005485767,0.0002308147,0.0000235398,0.0002392178,0.00001951198,0.0000677975,0.00003708642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002283994,"about_ca_system_score_gemma":0.00006666029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004480804,"about_ca_topic_score_gemma":0.0000677912,"domain_scores_codex":[0.9990007,0.000139305,0.0002836499,0.0002567306,0.0001450442,0.0001746058],"domain_scores_gemma":[0.9987597,0.0006149809,0.0001420975,0.0003150353,0.0001409851,0.00002716561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002265636,0.000135472,0.01523486,0.00005841121,0.00006848077,6.214941e-7,0.0007119675,0.04948525,0.006319421,0.6746793,0.001490777,0.2515889],"study_design_scores_gemma":[0.002178963,0.0002267994,0.01068315,0.00002989687,0.00001443958,0.000001733699,0.00003910595,0.9827694,0.001015273,0.000633896,0.002287741,0.0001195645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158217,0.0001541587,0.8752935,0.006038707,0.0004321217,0.001259012,0.00000511279,0.0000811456,0.0009146057],"genre_scores_gemma":[0.9974214,0.00002152068,0.0007968496,0.0009503977,0.00004629136,0.00006766936,0.000009563059,0.000006306449,0.0006800402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9332842,"threshold_uncertainty_score":0.305614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007863758661103205,"score_gpt":0.2194982424690114,"score_spread":0.2116344838079082,"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."}}