{"id":"W2051835769","doi":"10.1145/1198513.1198523","title":"Optimally scheduling video-on-demand to minimize delay when sender and receiver bandwidth may differ","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Algorithms","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Communication source; Upper and lower bounds; Bandwidth (computing); Network packet; Scheduling (production processes); Computer network; Real-time computing; Bandwidth allocation; Channel (broadcasting); Algorithm; Mathematics; Mathematical optimization","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008311943,0.0003144029,0.0002587176,0.0002102317,0.0001882496,0.0000671745,0.000166675,0.0001589709,0.0001249788],"category_scores_gemma":[0.00001210707,0.0003232996,0.00006873353,0.0002578402,0.00003918165,0.0002321898,0.000006777232,0.0003045393,0.00005445373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097671,"about_ca_system_score_gemma":0.00001013278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001317991,"about_ca_topic_score_gemma":0.00001271319,"domain_scores_codex":[0.9986671,0.00003145612,0.0002950845,0.0004013545,0.0002248012,0.0003802193],"domain_scores_gemma":[0.9991555,0.000188127,0.00003006294,0.0004259814,0.00005284819,0.0001475156],"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":[0.00004349392,0.00005124437,0.00003243067,0.00001511543,0.00004925617,0.000009133279,0.0001308055,0.9551976,0.0003719479,0.00002588359,0.0002549363,0.04381821],"study_design_scores_gemma":[0.002117533,0.0001669457,0.002301462,0.0001778687,0.0001083135,0.00004154833,0.0000907603,0.9825587,0.00705535,0.001317473,0.003092857,0.0009711796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01730263,0.0001619329,0.980298,0.0005146051,0.0003976439,0.0003106926,0.00003502088,0.0004072671,0.000572202],"genre_scores_gemma":[0.4123557,0.0002401802,0.5862718,0.0002044479,0.0001673925,0.00008093497,0.00001898659,0.0001000687,0.0005605108],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3950531,"threshold_uncertainty_score":0.9999219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009493140495814548,"score_gpt":0.2184976349863621,"score_spread":0.2090044944905475,"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."}}