{"id":"W2094537059","doi":"10.1016/j.jmr.2009.11.006","title":"Variable bandwidth filtering for magnetic resonance imaging with pure phase encoding","year":2009,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada; United States Agency for International Development","keywords":"Bandwidth (computing); Amplitude; k-space; Filter (signal processing); Algorithm; Computer science; Mathematics; Encoding (memory); Control theory (sociology); Optics; Fourier transform; Physics; Artificial intelligence; Computer vision; Telecommunications; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005438848,0.0007599982,0.0004573362,0.0005293223,0.0003726464,0.0008385929,0.0007303345,0.001155036,0.003998381],"category_scores_gemma":[0.001986121,0.0005570801,0.0004163945,0.0008466662,0.000518684,0.001559664,0.0005863841,0.0008442574,0.00196807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003870427,"about_ca_system_score_gemma":0.0003987677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007674727,"about_ca_topic_score_gemma":0.001671129,"domain_scores_codex":[0.999743,0.00008695381,0.00001729975,0.00003771642,0.0000920188,0.00002304688],"domain_scores_gemma":[0.9992245,0.00050854,0.00006329507,0.0001033788,0.00008222531,0.00001813946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006839683,0.0001333544,0.0003467884,0.0005139963,0.00009021251,0.0001783199,0.0001442608,0.02757896,0.4473,0.08206736,0.00260966,0.438353],"study_design_scores_gemma":[0.00009733703,0.0003134414,0.0009106438,0.0001244802,0.0001644831,0.0009128106,0.0000493407,0.5767315,0.33917,0.04478459,0.03664701,0.00009434282],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006690291,0.0007912755,0.9900836,0.0001175455,0.00005481314,0.0000218301,0.00003353757,0.0003732597,0.00183385],"genre_scores_gemma":[0.06066478,0.001405838,0.9334663,0.0001031589,0.0001034229,0.00006961771,0.0001546662,0.0002562799,0.003775942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003998381,"threshold_uncertainty_score":0.01337588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01362270108519491,"score_gpt":0.3036509766467924,"score_spread":0.2900282755615975,"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."}}