{"id":"W2006867071","doi":"10.1049/el.2014.0626","title":"Robustness of Radon transform to white additive noise: general case study","year":2014,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robustness (evolution); Radon; White noise; Radon transform; Computer science; Mathematics; Acoustics; Electronic engineering; Engineering; Artificial intelligence; Telecommunications; Physics","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":[0.002872704,0.0008783345,0.001354874,0.001202585,0.0003776026,0.00156629,0.0008372504,0.002095995,0.001243356],"category_scores_gemma":[0.01139382,0.0003328695,0.001510997,0.0007833104,0.002102707,0.001123138,0.001194784,0.0008356968,0.0002274151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005506444,"about_ca_system_score_gemma":0.0003572762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001925912,"about_ca_topic_score_gemma":0.000693622,"domain_scores_codex":[0.9981883,0.0005749448,0.00009903257,0.0003878095,0.0005208345,0.000229128],"domain_scores_gemma":[0.9929402,0.005132634,0.0006367072,0.0006182536,0.0005519367,0.0001202966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000923304,0.0001119592,0.003422491,0.0004940248,0.0002955098,0.003227484,0.0002793213,0.8681755,0.05038356,0.02824521,0.0009108761,0.04353071],"study_design_scores_gemma":[0.00001760607,0.0003302901,0.00329173,0.00005064462,0.000143441,0.001966058,0.0001299594,0.9620887,0.01909195,0.01131722,0.00149006,0.00008242295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2800886,0.002924047,0.7068918,0.0004953229,0.0001114706,0.00008864844,0.0001595938,0.0005192583,0.008721226],"genre_scores_gemma":[0.9653005,0.001826019,0.03002689,0.00004954121,0.00009201574,0.0000359951,0.00009390803,0.0000823048,0.00249283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002872704,"threshold_uncertainty_score":0.01519251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009772644852216017,"score_gpt":0.2580710976895105,"score_spread":0.2482984528372945,"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."}}