{"id":"W2087515962","doi":"10.1109/tim.2012.2190550","title":"Energy-Efficient Compressive State Recovery From Sparsely Noisy Measurements","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Compressed sensing; Underdetermined system; Signal reconstruction; Wireless sensor network; Computer science; Energy (signal processing); Gaussian; SIGNAL (programming language); Signal processing; Algorithm; Mathematics; Telecommunications; 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":[0.0001653164,0.000233761,0.000176537,0.0001491744,0.0001559068,0.00005514203,0.00007214318,0.00006126111,0.00006943402],"category_scores_gemma":[0.000001474644,0.0002396866,0.00006605591,0.0001095718,0.00003421045,0.0001888931,0.00000141018,0.0001344801,0.00002191993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000261005,"about_ca_system_score_gemma":0.00001712913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001550893,"about_ca_topic_score_gemma":0.00005873781,"domain_scores_codex":[0.9985725,0.00006333357,0.0002827662,0.0002007108,0.0005816719,0.00029899],"domain_scores_gemma":[0.9994602,0.00001968503,0.00005857612,0.0001971823,0.00009853235,0.0001658728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002187467,0.0006736447,0.0003068632,0.00002974542,0.0007680679,0.000002691925,0.001675258,0.1876763,0.3440439,0.00004294806,0.002142044,0.4624198],"study_design_scores_gemma":[0.0008966918,0.00008348788,0.001403788,0.0001447415,0.00009890794,0.000004121667,0.0001559475,0.01320554,0.9817218,0.0001188829,0.001806643,0.0003594755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3267219,0.0003432779,0.6692892,0.00003031088,0.001609138,0.0002272621,0.00004360774,0.0003976767,0.001337586],"genre_scores_gemma":[0.9978877,0.0002297263,0.001493969,0.0001992782,0.00005131319,0.00006981415,0.000008011447,0.00003407175,0.00002607999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6711658,"threshold_uncertainty_score":0.9774135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07738180498407746,"score_gpt":0.235482785200147,"score_spread":0.1581009802160696,"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."}}