{"id":"W2277762866","doi":"","title":"Resolution Enhancement in Magnetic Resonance Imaging by Frequency Extrapolation","year":2008,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Extrapolation; Fourier transform; k-space; Image resolution; Resolution (logic); Frequency domain; Similarity (geometry); Computer science; Algorithm; Artificial intelligence; Mathematics; Computer vision; Image (mathematics); Statistics; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002828424,0.0002090739,0.0002892017,0.0003784208,0.0001816877,0.00003578676,0.0006854395,0.0001545541,0.00005127778],"category_scores_gemma":[0.00001528156,0.0002707301,0.00009333283,0.0004356107,0.00006068349,0.0006777345,0.00006132489,0.0002533905,0.00002515584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001668457,"about_ca_system_score_gemma":0.0001081467,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02255113,"about_ca_topic_score_gemma":0.005032514,"domain_scores_codex":[0.9984166,0.0001861787,0.0001945784,0.0005104918,0.0003790437,0.0003131263],"domain_scores_gemma":[0.9991427,0.00003801087,0.0002169291,0.0004063437,0.0001398683,0.00005614362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003491784,0.0003752934,0.001857392,0.0003226108,0.00002226188,0.0005281781,0.23305,0.00004733845,0.3340376,0.001864261,0.01396967,0.4135762],"study_design_scores_gemma":[0.02542741,0.003028042,0.326618,0.00864699,0.0004839117,0.0001701764,0.1072417,0.2144499,0.2423231,0.02832122,0.03293027,0.01035928],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.9034958,0.01952624,0.0706528,0.0009767456,0.0008840881,0.0006130571,0.00001926519,0.0001470724,0.003684976],"genre_scores_gemma":[0.09408608,0.002768045,0.2488893,0.00009091314,0.00006818417,0.000002880385,0.0005457142,0.00005209646,0.6534969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8094097,"threshold_uncertainty_score":0.9999745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009797056286422332,"score_gpt":0.2167687743327517,"score_spread":0.2069717180463293,"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."}}