{"id":"W2084082172","doi":"10.1109/jstars.2013.2285383","title":"Geophysical Signal Parameterization and Filtering Using the fractional Fourier Transform","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Mathematical Analysis and Transform Methods","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Fractional Fourier transform; Fourier transform; Frequency domain; Computer science; Signal processing; Time–frequency analysis; Short-time Fourier transform; Noise (video); Algorithm; SIGNAL (programming language); Geology; Filter (signal processing); Mathematics; Fourier analysis; Artificial intelligence; Mathematical analysis; Telecommunications; Image (mathematics); Computer vision; Radar","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":[0.0005020811,0.0005162674,0.000475619,0.001013001,0.0003495094,0.001101308,0.0004380821,0.0008206188,0.001490397],"category_scores_gemma":[0.002020527,0.000205271,0.0006190865,0.001450119,0.0005414712,0.001297522,0.0004838472,0.0008371386,0.0006326609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002957901,"about_ca_system_score_gemma":0.0003909227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001686723,"about_ca_topic_score_gemma":0.0009276169,"domain_scores_codex":[0.9998085,0.00004804037,0.00001355498,0.00004542665,0.00006550971,0.00001892757],"domain_scores_gemma":[0.9997694,0.0001149943,0.00002814825,0.00004320589,0.00003797918,0.000006317091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001459483,0.00006201548,0.001317214,0.0001786926,0.00006261768,0.0003602544,0.00021441,0.2878695,0.07684948,0.1038855,0.00245508,0.5265993],"study_design_scores_gemma":[0.000008373348,0.00004175525,0.001299004,0.00001971991,0.00001849628,0.0002678517,0.00005307043,0.9373581,0.0132658,0.03840622,0.00922335,0.0000382315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006791107,0.0001916474,0.9918149,0.00008301919,0.0000282057,0.00001086814,0.00004334782,0.0002391825,0.0007976645],"genre_scores_gemma":[0.2695844,0.001659401,0.7244601,0.0001041192,0.000202163,0.0000968747,0.0003597446,0.0002162116,0.0033169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001686723,"threshold_uncertainty_score":0.004985809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06385048782466479,"score_gpt":0.2950030247735649,"score_spread":0.2311525369489001,"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."}}