{"id":"W1990243878","doi":"10.1007/s11276-010-0285-8","title":"Application layer QoS optimization for multimedia transmission over cognitive radio networks","year":2010,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Carleton University","funders":"Defense Advanced Research Projects Agency","keywords":"Computer science; Cognitive radio; Quality of service; Partially observable Markov decision process; Channel (broadcasting); Markov decision process; Transmission (telecommunications); Computer network; Wireless; Multimedia; Markov process; Markov model; Markov chain; Telecommunications; Machine learning","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":[0.0004304866,0.0003531184,0.0003651195,0.0001139732,0.0004343322,0.0002653658,0.0004808781,0.0004078724,0.00004374171],"category_scores_gemma":[0.00002648325,0.0003456069,0.0001901683,0.0005960256,0.0001047677,0.00049793,0.00008203255,0.0006499354,0.000004567848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004338624,"about_ca_system_score_gemma":0.00005351224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002152006,"about_ca_topic_score_gemma":0.00004789049,"domain_scores_codex":[0.9976907,0.00008622421,0.0004237351,0.0008278103,0.0002708252,0.0007007078],"domain_scores_gemma":[0.9981306,0.000664948,0.0002275603,0.0004485601,0.0002644727,0.0002638215],"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":[0.00008024557,0.0000867412,0.0002719045,0.00000876437,0.00003832589,0.000005188063,0.0001616874,0.3524515,0.0004129055,0.002605578,0.001016004,0.6428612],"study_design_scores_gemma":[0.001489814,0.00006574509,0.00152146,0.00008007384,0.00004296214,0.0000188661,0.00001023187,0.9940634,0.0002755489,0.0001515323,0.001827182,0.0004531305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006224879,0.0003054997,0.9898783,0.0002519682,0.00153426,0.001145119,0.000004358195,0.0003370363,0.0003185719],"genre_scores_gemma":[0.9366238,0.0002123191,0.05980359,0.0004045964,0.002537153,0.0001492492,0.0001453718,0.00006031102,0.00006363615],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9303989,"threshold_uncertainty_score":0.9998996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008221511219649393,"score_gpt":0.2385539700797366,"score_spread":0.2303324588600872,"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."}}