{"id":"W1977159052","doi":"10.1016/j.physleta.2012.10.018","title":"Exact Fourier spectrum recovery","year":2012,"lang":"en","type":"article","venue":"Physics Letters A","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Discrete Fourier transform (general); Discrete-time Fourier transform; Non-uniform discrete Fourier transform; Fourier transform; Algorithm; Spectrum (functional analysis); Discrete Fourier series; Fourier analysis; SIGNAL (programming language); Spectral density estimation; Sampling (signal processing); Signal processing; Frequency spectrum; Multidimensional signal processing; Noise (video); Short-time Fourier transform; Fractional Fourier transform; Computer science; Mathematics; Mathematical analysis; Physics; Spectral density; Telecommunications; Artificial intelligence","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":[0.0005911884,0.0007750228,0.0006940697,0.0007745019,0.0003420351,0.001052274,0.0005623378,0.001156149,0.007265158],"category_scores_gemma":[0.003309559,0.0003766154,0.0003948996,0.0008688685,0.001101051,0.002576944,0.001654785,0.001470577,0.002552866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002768637,"about_ca_system_score_gemma":0.0006840244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006109143,"about_ca_topic_score_gemma":0.0007775935,"domain_scores_codex":[0.9995048,0.00009476557,0.00002647413,0.0001026547,0.0002266375,0.00004466175],"domain_scores_gemma":[0.9993175,0.0001892593,0.00005502746,0.0002850757,0.0001236263,0.00002947066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000303275,0.0001060101,0.0005344742,0.0002440438,0.00007517241,0.0002093819,0.0001266748,0.08831707,0.02849092,0.4700807,0.01486362,0.3966488],"study_design_scores_gemma":[0.00003468729,0.00004479349,0.0005308787,0.00004442016,0.00002175844,0.0005502153,0.00005902268,0.7050328,0.01903229,0.2622597,0.01235016,0.00003935198],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01095253,0.000468593,0.9724082,0.0006525214,0.0002404861,0.00002836029,0.0002265816,0.0003247706,0.01469784],"genre_scores_gemma":[0.4016075,0.001597152,0.5521589,0.000599784,0.0004939836,0.00008650385,0.001032249,0.0003223773,0.0421016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007265158,"threshold_uncertainty_score":0.02430433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014002153130693,"score_gpt":0.2086753214287291,"score_spread":0.1946731682980361,"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."}}