{"id":"W2639424157","doi":"10.1016/j.jfranklin.2017.06.009","title":"A new sensor selection scheme for Bayesian learning based sparse signal recovery in WSNs","year":2017,"lang":"en","type":"article","venue":"Journal of the Franklin Institute","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Graduate Research and Innovation Projects of Jiangsu Province; Jiangsu University of Science and Technology; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Compressed sensing; Wireless sensor network; Overhead (engineering); Computer science; Selection (genetic algorithm); Estimator; Reduction (mathematics); Bayesian probability; SIGNAL (programming language); Algorithm; Energy (signal processing); Bayesian inference; Mathematical optimization; Machine learning; Artificial intelligence; Mathematics; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002790777,0.0001206236,0.0002108758,0.0001280731,0.0002181794,0.0001064613,0.0003274548,0.00009506642,0.00001426324],"category_scores_gemma":[0.0001979623,0.00009592327,0.0001792201,0.00007101365,0.00003068224,0.0003409129,0.00002523593,0.0004453966,0.000001767944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008684606,"about_ca_system_score_gemma":0.0001088279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005912469,"about_ca_topic_score_gemma":0.0000879971,"domain_scores_codex":[0.9992784,0.00002718634,0.0002957099,0.00008095343,0.0001465403,0.0001711704],"domain_scores_gemma":[0.9993752,0.00004331359,0.000250241,0.0001938694,0.00008173762,0.00005568957],"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":[0.0002785409,0.00003969782,0.007983504,0.00004198054,0.0001342669,0.00004234472,0.0001023879,0.8916698,0.06713496,0.0001164012,0.007843373,0.02461278],"study_design_scores_gemma":[0.002547544,0.0002748409,0.009982608,0.001353217,0.0000998906,0.0001553732,0.00002175051,0.7351723,0.1705049,0.00295386,0.0765148,0.0004189642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2269159,0.0001523733,0.7690623,0.0006085813,0.001973019,0.0002871381,0.000001861083,0.0000997337,0.0008990443],"genre_scores_gemma":[0.9248049,0.00002945658,0.07432492,0.00005713106,0.0005904594,0.000003335763,4.147922e-7,0.00002311302,0.000166301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.697889,"threshold_uncertainty_score":0.3911637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02146587888860297,"score_gpt":0.251961579268715,"score_spread":0.2304957003801121,"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."}}