{"id":"W2236225132","doi":"10.1002/mrm.26071","title":"Q‐space truncation and sampling in diffusion spectrum imaging","year":2016,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Eye Institute; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Canadian Institutes of Health Research; National Institutes of Health; Consortia for Improving Medicine with Innovation and Technology","keywords":"Sampling (signal processing); Truncation (statistics); Spectrum (functional analysis); Diffusion; Nuclear magnetic resonance; Diffusion MRI; k-space; Space (punctuation); Physics; Statistical physics; Computer science; Mathematics; Mathematical analysis; Statistics; Medicine; Magnetic resonance imaging; Optics; Radiology; Fourier transform; Quantum mechanics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002485474,0.0005417748,0.0004047999,0.0003225539,0.0003422638,0.0004986291,0.0004126533,0.0003916619,0.0005802891],"category_scores_gemma":[0.01163113,0.000322082,0.0002857573,0.0003095672,0.001172752,0.001330517,0.000576922,0.0004733735,0.0001998011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005381766,"about_ca_system_score_gemma":0.0006396903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422887,"about_ca_topic_score_gemma":0.0007874761,"domain_scores_codex":[0.9995964,0.000141637,0.00002905109,0.00006190419,0.0001290933,0.00004197048],"domain_scores_gemma":[0.9962924,0.002330583,0.0004728521,0.0004437467,0.0003423494,0.0001180375],"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":[0.00143525,0.0001603395,0.014302,0.0005758746,0.0001079588,0.001268958,0.0008644655,0.1625802,0.675964,0.02373133,0.0007051047,0.1183046],"study_design_scores_gemma":[0.00009301418,0.0007578918,0.02680976,0.0000773408,0.00006319949,0.002449881,0.000117786,0.604052,0.3438225,0.01857267,0.003080723,0.0001032805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3573099,0.0006385079,0.6403896,0.000149504,0.00002347254,0.00007704457,0.0001014468,0.0002961519,0.001014298],"genre_scores_gemma":[0.8476399,0.0006682589,0.1502916,0.00009501928,0.00002495476,0.00008955353,0.0002345156,0.0001636507,0.0007924854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002485474,"threshold_uncertainty_score":0.01314455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04134616623408731,"score_gpt":0.3431390002351696,"score_spread":0.3017928340010823,"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."}}