{"id":"W2075123782","doi":"10.1016/j.sigpro.2014.10.005","title":"Consensus-based distributed dynamic sensor selection in decentralised sensor networks using the posterior Cramér–Rao lower bound","year":2014,"lang":"en","type":"article","venue":"Signal Processing","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Wireless sensor network; Computer science; Selection (genetic algorithm); Algorithm; Upper and lower bounds; Overhead (engineering); Sensor array; Mathematics; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001988281,0.0007858621,0.001950687,0.0008271518,0.0007208881,0.001456809,0.001419052,0.0009449769,0.001564803],"category_scores_gemma":[0.009639341,0.0007016379,0.0006203685,0.001398275,0.001352826,0.002229877,0.001690687,0.001696308,0.0004051833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009703744,"about_ca_system_score_gemma":0.001765338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003556468,"about_ca_topic_score_gemma":0.00306215,"domain_scores_codex":[0.9982098,0.0005194999,0.00009009991,0.0004520392,0.0005732555,0.0001552948],"domain_scores_gemma":[0.9949359,0.003388236,0.0003608795,0.0004304634,0.0007531564,0.0001313182],"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":[0.00014485,0.00003570629,0.0002999684,0.00009686991,0.00005816334,0.00004718047,0.00009821183,0.9378924,0.002805203,0.01776766,0.001148124,0.03960576],"study_design_scores_gemma":[0.00001039914,0.00002023329,0.00007951996,0.000004636649,0.000006575618,0.00001454394,0.000008359302,0.9924757,0.0005964122,0.006561515,0.0002151969,0.000006955033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005562175,0.0001699065,0.9932335,0.00009682895,0.00002483972,0.00001412264,0.00001393299,0.0001244008,0.0007602312],"genre_scores_gemma":[0.8257419,0.0005323073,0.1699887,0.0001093206,0.000117512,0.0001673551,0.0001545174,0.0001190561,0.003069365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003556468,"threshold_uncertainty_score":0.01051515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01186049442167381,"score_gpt":0.2485408718705411,"score_spread":0.2366803774488673,"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."}}