{"id":"W1602326336","doi":"10.1016/j.dam.2003.02.002","title":"Worst-case analysis of a dynamic channel assignment strategy","year":2004,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Competitive analysis; Reuse; Channel (broadcasting); Greedy algorithm; Algorithm; Computer science; Online algorithm; Mathematical optimization; Upper and lower bounds; Channel allocation schemes; Mathematics; Theoretical computer science; Computer network; Engineering; Wireless; Telecommunications","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":[0.005729035,0.00237176,0.002278475,0.002147522,0.001419873,0.005320254,0.003925647,0.002573242,0.01340883],"category_scores_gemma":[0.02205807,0.001253905,0.001352739,0.002316125,0.002475098,0.004296366,0.002379289,0.002295463,0.001148083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004077811,"about_ca_system_score_gemma":0.003530638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005543599,"about_ca_topic_score_gemma":0.00392553,"domain_scores_codex":[0.9941789,0.002046264,0.0001480003,0.000587608,0.001200311,0.001838869],"domain_scores_gemma":[0.9753197,0.0191561,0.001325681,0.001041561,0.001994727,0.00116221],"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":[0.0008313812,0.0002348543,0.0009205519,0.0002289233,0.0001581698,0.0003724474,0.0001324534,0.8720438,0.003419299,0.1025004,0.005852687,0.01330503],"study_design_scores_gemma":[0.00003581043,0.00009376891,0.0001707012,0.00001834992,0.00005038007,0.000131482,0.00008149108,0.9652513,0.0005381962,0.03302897,0.0005767355,0.00002286881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1828869,0.001794663,0.7302036,0.002891387,0.0003128682,0.0004702628,0.0009428206,0.0006137866,0.07988373],"genre_scores_gemma":[0.9408316,0.000750338,0.04202191,0.0004462065,0.0002830491,0.0002290449,0.000328224,0.0002505785,0.01485892],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01340883,"threshold_uncertainty_score":0.04485703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02822677562133897,"score_gpt":0.3024998840984041,"score_spread":0.2742731084770652,"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."}}