{"id":"W4220892781","doi":"10.32920/19400750.v1","title":"Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Canada First Research Excellence Fund; Canada Research Chairs","keywords":"Diffusion MRI; Fractional anisotropy; Anisotropy; Orientation (vector space); Metric (unit); Linear regression; Diffusion; Tensor (intrinsic definition); Dispersion (optics); SIGNAL (programming language); Biological system; Nuclear magnetic resonance; Physics; Materials science; Statistical physics; Mathematics; Computer science; Optics; Statistics; Magnetic resonance imaging; Geometry; Radiology; Medicine; Biology","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":[0.001152615,0.001333841,0.0006669314,0.0005577397,0.0002126299,0.0007884387,0.001115548,0.0009894567,0.003745855],"category_scores_gemma":[0.002188931,0.0005531246,0.0007508522,0.0006986487,0.0005245046,0.001229135,0.0008948774,0.001586734,0.001851125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005344111,"about_ca_system_score_gemma":0.001245992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00235527,"about_ca_topic_score_gemma":0.003625351,"domain_scores_codex":[0.999594,0.0001169227,0.00001832057,0.000084963,0.0001519718,0.00003385511],"domain_scores_gemma":[0.999486,0.0002297081,0.00008471507,0.0000552058,0.0001228239,0.000021537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001769202,0.0001378366,0.0005686124,0.0003370077,0.0002108135,0.0003352626,0.0001381147,0.5982505,0.08594558,0.105248,0.01142463,0.1972267],"study_design_scores_gemma":[0.000009410983,0.00003518642,0.0001041721,0.000007651308,0.00001399651,0.00006333845,0.000005628157,0.9827124,0.005988104,0.00653539,0.004508533,0.00001619708],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002144928,0.00009481197,0.9963749,0.0001184271,0.00001530323,0.0000241987,0.00008473511,0.0004108901,0.0007319082],"genre_scores_gemma":[0.05078571,0.0004991097,0.9394618,0.0001394491,0.00009152255,0.0002335208,0.0004857737,0.0007995291,0.007503554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003745855,"threshold_uncertainty_score":0.0125311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07503130547690681,"score_gpt":0.4057850943838871,"score_spread":0.3307537889069803,"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."}}