{"id":"W2019462839","doi":"10.1016/j.disc.2008.05.025","title":"Generating asymptotically optimal broadcasting schedules to minimize average waiting time","year":2008,"lang":"en","type":"article","venue":"Discrete Mathematics","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Schedule; Asymptotically optimal algorithm; Scheduling (production processes); Mathematical optimization; Computer science; Broadcasting (networking); Block (permutation group theory); Mathematics; Heuristic; Algorithm; Combinatorics; Computer network","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":[0.0001508464,0.0003006221,0.0003545537,0.00009039877,0.0002403819,0.0000649663,0.0002023043,0.000101759,0.00009155199],"category_scores_gemma":[0.0002820718,0.0003135568,0.00007890798,0.000284646,0.00004264506,0.0002065438,0.00009850354,0.0002063768,0.0002225524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007418536,"about_ca_system_score_gemma":0.00001681588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.608469e-7,"about_ca_topic_score_gemma":2.445095e-7,"domain_scores_codex":[0.9983976,0.00002231395,0.0005401307,0.000253872,0.0002878208,0.0004982527],"domain_scores_gemma":[0.9991558,0.0002130219,0.00008141677,0.0003029099,0.00007481133,0.0001720459],"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":[0.000002575814,0.0000138359,0.000032469,0.00008290185,0.00002738608,0.00002008478,0.001021842,0.9910415,0.005925164,0.0007769064,0.00008514064,0.0009702421],"study_design_scores_gemma":[0.0001995543,0.00001684592,0.00001746878,0.0001801617,0.00001501421,0.00004712101,0.0001122626,0.9964518,0.002450156,0.00009061609,0.00004660296,0.0003724293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3193738,0.00005143059,0.6774905,0.00002306135,0.00006992235,0.0001814836,0.000007173725,0.0004774008,0.002325296],"genre_scores_gemma":[0.3297769,0.00001724827,0.6696377,0.00004219933,0.000206747,0.00002286016,0.00002403024,0.0001036784,0.0001686709],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0104031,"threshold_uncertainty_score":0.9999316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01167587249279253,"score_gpt":0.2139477419805361,"score_spread":0.2022718694877436,"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."}}