{"id":"W1588008030","doi":"10.5281/zenodo.38447","title":"Thresholding-Based Sampling And Signal Reconstruction From Multiple Noisy Observations","year":2004,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Oversampling; Signal reconstruction; Bandlimiting; Nyquist rate; SIGNAL (programming language); Undersampling; Sampling (signal processing); Computer science; Noise (video); Nyquist frequency; Algorithm; Thresholding; Compressed sensing; Reconstruction filter; Nyquist–Shannon sampling theorem; Discrete-time signal; Artificial intelligence; Iterative reconstruction; Signal processing; Mathematics; Computer vision; Analog signal; Signal transfer function; Digital filter; Fourier transform; Filter (signal processing); Bandwidth (computing); Digital signal processing; Telecommunications; Image (mathematics)","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":[0.0002266812,0.00009663566,0.0001063285,0.00007271617,0.0002107117,0.0002331843,0.0002166687,0.00005127169,0.00002046934],"category_scores_gemma":[0.00006691255,0.00008898744,0.00003742065,0.0002143099,0.00004245855,0.00055213,0.0000584385,0.00009929413,0.000009185039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003331811,"about_ca_system_score_gemma":0.00007051237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006159226,"about_ca_topic_score_gemma":0.00006656797,"domain_scores_codex":[0.9991991,0.00003991077,0.00016713,0.0003021247,0.0001343018,0.0001574156],"domain_scores_gemma":[0.9993067,0.0002795719,0.00004708472,0.0002337231,0.00006693419,0.00006594707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005489108,0.0001654253,0.04636548,0.00002155473,0.00005373265,0.00003028917,0.001069433,0.04632557,0.4467449,0.04372058,0.0001011767,0.415347],"study_design_scores_gemma":[0.004061404,0.0001283991,0.08773037,0.0001326974,0.00002319153,0.00003910467,0.00007289716,0.5195134,0.2046726,0.1823751,0.0006476095,0.0006031954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2266843,0.00005467375,0.7720516,0.0006459483,0.000173322,0.00005929698,0.000002145157,0.0001346572,0.0001940614],"genre_scores_gemma":[0.4324858,0.000001660151,0.5669684,0.0004740315,0.00004351063,0.000002919615,0.000002721903,0.000003692912,0.00001731274],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4731878,"threshold_uncertainty_score":0.3628802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07281376107141639,"score_gpt":0.2799683570461453,"score_spread":0.2071545959747289,"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."}}