{"id":"W2408226875","doi":"10.1155/2016/3595389","title":"Distributed Channel-Aware Quantization Based on Maximum Mutual Information","year":2016,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Mutual information; Quantization (signal processing); Fusion center; Channel (broadcasting); Information theory; Algorithm; Artificial intelligence; Wireless; Cognitive radio; Mathematics; 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":[0.00179963,0.0008963032,0.001453358,0.0007069945,0.0005831612,0.0009678519,0.001532132,0.001022605,0.001530988],"category_scores_gemma":[0.005716739,0.0005035489,0.0005721592,0.0007999585,0.001346647,0.002209827,0.001820703,0.00116586,0.0003524668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001015569,"about_ca_system_score_gemma":0.0009720501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009967606,"about_ca_topic_score_gemma":0.001443843,"domain_scores_codex":[0.997929,0.0006798047,0.0001040473,0.000350402,0.0008090986,0.0001277121],"domain_scores_gemma":[0.9978384,0.001375768,0.0001830032,0.0002226439,0.0003226669,0.00005752684],"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":[0.0002119135,0.00006800367,0.0004300104,0.0001497515,0.00006172425,0.00007331628,0.0002013837,0.8032355,0.01366823,0.052777,0.001844198,0.127279],"study_design_scores_gemma":[0.00001704507,0.00003462276,0.00005680518,0.000008975953,0.000007427756,0.00002644101,0.000007480082,0.9837624,0.003003874,0.01253387,0.0005274754,0.00001352234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00334,0.000178893,0.9953315,0.00007922315,0.00001566214,0.00002197322,0.00001411194,0.0001493428,0.0008693816],"genre_scores_gemma":[0.6179772,0.0003749974,0.3789709,0.0002016316,0.00008815045,0.000215056,0.0001028723,0.0001135784,0.001955612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00179963,"threshold_uncertainty_score":0.009517431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008649903819096617,"score_gpt":0.2277363409631998,"score_spread":0.2190864371441032,"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."}}