{"id":"W2889215878","doi":"10.1016/j.automatica.2018.01.023","title":"Transmit power control and remote state estimation with sensor networks: A Bayesian inference approach","year":2018,"lang":"en","type":"article","venue":"Automatica","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimator; Control theory (sociology); Transmitter power output; Network packet; Controller (irrigation); Power control; Minimum-variance unbiased estimator; Computer science; Maximum a posteriori estimation; Wireless sensor network; Kalman filter; Mathematical optimization; Mathematics; Power (physics); Statistics; Control (management); Artificial intelligence; 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":[0.003331306,0.001485383,0.002327696,0.001274033,0.0007322204,0.002003388,0.002598265,0.001944698,0.002403541],"category_scores_gemma":[0.01318603,0.001557748,0.001477512,0.001603686,0.002362133,0.004025297,0.001739139,0.002976243,0.0004661986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001471537,"about_ca_system_score_gemma":0.00151461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007748989,"about_ca_topic_score_gemma":0.006612132,"domain_scores_codex":[0.9983602,0.000623633,0.00008184966,0.0004418439,0.0003831779,0.000109183],"domain_scores_gemma":[0.9942602,0.004764826,0.00036145,0.0002233985,0.0003253323,0.00006469477],"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":[0.00007502743,0.00007037159,0.0004662984,0.0001117289,0.0001185453,0.00005706452,0.00008343257,0.8994274,0.0008176407,0.06013355,0.000730569,0.03790838],"study_design_scores_gemma":[0.0000098419,0.00001153658,0.0001280787,0.00001075021,0.00001826285,0.00001334295,0.000005464531,0.968801,0.0002502382,0.03051875,0.0002206548,0.00001212455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002129865,0.0002390947,0.9967648,0.0001698117,0.00001764755,0.00001110609,0.0000216783,0.00005581476,0.0005903351],"genre_scores_gemma":[0.6813084,0.002528078,0.3059237,0.000337436,0.0006620726,0.0003224433,0.0003031169,0.0002094858,0.008405304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007748989,"threshold_uncertainty_score":0.01761782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005443387671075614,"score_gpt":0.2164263994661475,"score_spread":0.2109830117950719,"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."}}