{"id":"W2964023423","doi":"","title":"COMPRESSIVE SAMPLING FOR ENERGY SPECTRUM ESTIMATION OF TURBULENT FLOWS","year":2015,"lang":"en","type":"article","venue":"MacSphere (McMaster University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Nyquist–Shannon sampling theorem; Compressed sensing; Mathematics; Wavelet; Sampling (signal processing); Energy (signal processing); Signal reconstruction; Matching pursuit; Algorithm; Bandlimiting; Spectral density; Fourier transform; Mathematical analysis; Signal processing; Statistics; Computer science; Filter (signal processing)","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":[0.0006756996,0.0003863484,0.000307214,0.0005935149,0.0002352345,0.0003411788,0.0003125759,0.0004240116,0.0006744562],"category_scores_gemma":[0.00356511,0.0001904831,0.0001949151,0.0005209886,0.0004791429,0.0006505272,0.0005730335,0.0006136225,0.0001641358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624012,"about_ca_system_score_gemma":0.0004228966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522262,"about_ca_topic_score_gemma":0.001561882,"domain_scores_codex":[0.9997146,0.0001003073,0.00001297154,0.00003059579,0.0001246857,0.00001687602],"domain_scores_gemma":[0.999288,0.0004847495,0.00006024394,0.00005407983,0.00009133987,0.00002155459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002938021,0.00006853502,0.001351646,0.0001421222,0.00003500443,0.00009469114,0.0001038879,0.6510244,0.04341615,0.04000964,0.001906522,0.2615536],"study_design_scores_gemma":[0.000005130382,0.0000145938,0.0001877907,0.000005351563,0.000001691123,0.00001598777,0.000005549736,0.991497,0.002865949,0.004891359,0.0005045732,0.00000500589],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02389942,0.0003843545,0.9743332,0.0001852304,0.00002814854,0.00001579005,0.00003485617,0.0001460019,0.0009730915],"genre_scores_gemma":[0.5743572,0.0007789285,0.4234257,0.00009227348,0.0001049604,0.00005791004,0.0001695658,0.00005057993,0.000962879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001522262,"threshold_uncertainty_score":0.003573477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02890264718155161,"score_gpt":0.2150317863083241,"score_spread":0.1861291391267725,"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."}}