{"id":"W2101644183","doi":"10.1109/ccnc.2006.1593081","title":"End-to-end loss discrimination for improved throughput performance in heterogeneous networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Network Traffic and Congestion Control","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Throughput; End-to-end principle; Computer science; Computer network; Telecommunications; Wireless","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":[0.0002111509,0.0001470798,0.000159247,0.00007502912,0.00009844491,0.0001216967,0.0004340696,0.00006808021,0.00001362839],"category_scores_gemma":[0.000005870891,0.0001304051,0.00005946511,0.0002725344,0.00002399037,0.0003353854,0.0000771162,0.0000819409,0.00001243806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005687394,"about_ca_system_score_gemma":0.00002767147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004883015,"about_ca_topic_score_gemma":0.0005037072,"domain_scores_codex":[0.9987966,0.00002595722,0.0002716815,0.0003915482,0.0001151503,0.0003990187],"domain_scores_gemma":[0.9994268,0.00009370617,0.00005637857,0.0003025035,0.00006160699,0.00005905095],"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":[0.00003708076,0.00008564686,0.0008355109,0.000009251045,0.000007064637,0.000003962804,0.00008039074,0.1826184,0.00006302697,0.04087108,0.0007387488,0.7746498],"study_design_scores_gemma":[0.0006505575,0.0001278863,0.003746506,0.00001222778,0.000004316717,0.000008793346,0.000003983267,0.9898018,0.0002153958,0.0005537632,0.004690247,0.0001844841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04051881,0.0001086209,0.955443,0.001655044,0.0004344703,0.000490579,0.000001627988,0.000154094,0.001193779],"genre_scores_gemma":[0.983506,0.00001410317,0.01447025,0.0007066635,0.0002831697,0.0001381651,0.00001109599,0.0000103202,0.0008602864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9429871,"threshold_uncertainty_score":0.5317767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008450721101687703,"score_gpt":0.2183381290111384,"score_spread":0.2098874079094507,"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."}}