{"id":"W2973683106","doi":"10.1002/mrm.28232","title":"High‐fidelity, accelerated whole‐brain submillimeter in vivo diffusion MRI using gSlider‐spherical ridgelets (gSlider‐SR)","year":2020,"lang":"en","type":"preprint","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Mental Health","keywords":"Scanner; Computer science; Diffusion MRI; Redundancy (engineering); Human Connectome Project; Noise (video); Data acquisition; Angular resolution (graph drawing); Image resolution; Signal-to-noise ratio (imaging); Monte Carlo method; SIGNAL (programming language); Artificial intelligence; Computer vision; Physics; Magnetic resonance imaging; Mathematics","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":[0.0007187387,0.0004974002,0.0003715901,0.0002733506,0.0001164481,0.0003624178,0.0004700847,0.0005004544,0.0008396451],"category_scores_gemma":[0.001247521,0.0003810953,0.0004637351,0.0003303133,0.0002652237,0.0005933882,0.0004666425,0.0007036596,0.0004489734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002266765,"about_ca_system_score_gemma":0.0005523228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006509589,"about_ca_topic_score_gemma":0.001227296,"domain_scores_codex":[0.9998453,0.00003671405,0.000009698906,0.00002369682,0.00007186355,0.00001279194],"domain_scores_gemma":[0.9996344,0.0001039606,0.00009884612,0.00007143259,0.00006386552,0.00002756105],"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":[0.0002675505,0.00008877231,0.002256924,0.0004748016,0.0001280922,0.0005377219,0.0001976187,0.1531967,0.6610969,0.006987826,0.003138232,0.1716289],"study_design_scores_gemma":[0.00003667023,0.0002014091,0.002848963,0.00003004591,0.00004475969,0.001256066,0.00002913761,0.7994811,0.1877268,0.002386339,0.005889215,0.00006952293],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04096989,0.0002062217,0.9573436,0.0001428493,0.00002111199,0.00003629132,0.00009652053,0.0006602117,0.0005233355],"genre_scores_gemma":[0.1621049,0.0003314479,0.8360193,0.00008855877,0.00002373822,0.00006798952,0.0002219791,0.0001966592,0.0009454091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0008396451,"threshold_uncertainty_score":0.003801107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080150025691935,"score_gpt":0.3738312845897153,"score_spread":0.2658162820205219,"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."}}