{"id":"W2081747299","doi":"10.1016/j.neuroimage.2007.09.060","title":"Hybrid two-dimensional navigator correction: A new technique to suppress respiratory-induced physiological noise in multi-shot echo-planar functional MRI","year":2007,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Communication noise; Echo (communications protocol); Noise (video); A priori and a posteriori; Computer science; Statistical power; Functional magnetic resonance imaging; Magnetic resonance imaging; Planar; Artificial intelligence; Physics; Pattern recognition (psychology); Nuclear magnetic resonance; Acoustics; Statistics; Mathematics; Image (mathematics); Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002647896,0.0002606943,0.0003263647,0.0002094405,0.000117842,0.00001465315,0.0001435369,0.0001173199,0.0001536087],"category_scores_gemma":[0.0001498869,0.0002403793,0.0001086189,0.000420969,0.00005829081,0.0001264481,0.00007678367,0.0006872225,0.00009047017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001381997,"about_ca_system_score_gemma":0.0001225798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000652356,"about_ca_topic_score_gemma":0.00001853726,"domain_scores_codex":[0.9981316,0.00004259144,0.0004267778,0.0006724744,0.0003089118,0.0004176536],"domain_scores_gemma":[0.9987645,0.0001157207,0.0000899997,0.0004962913,0.0001228282,0.0004107218],"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":[0.000382269,0.0003532942,0.0004228915,0.000006838307,0.000003014387,0.0002632695,0.0000147694,0.0002509261,0.9780471,0.0000781327,0.01890994,0.001267576],"study_design_scores_gemma":[0.002939939,0.00106076,0.0944706,0.0001659371,0.00003365204,0.0006275692,0.00002992075,0.00111742,0.8681768,0.0004049397,0.03043164,0.0005408572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6195464,0.00003470389,0.3736416,0.001559362,0.0004801547,0.00273629,0.00004263538,0.0007692766,0.001189635],"genre_scores_gemma":[0.9154242,0.000003346601,0.07641459,0.006336178,0.0004888207,0.0003066102,0.00007942886,0.000061786,0.0008849782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.297227,"threshold_uncertainty_score":0.9802383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08471793144520143,"score_gpt":0.3671700932125613,"score_spread":0.2824521617673599,"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."}}