{"id":"W2114187925","doi":"10.1002/mrm.22292","title":"Tensor kernels for simultaneous fiber model estimation and tractography","year":2010,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health","keywords":"Diffusion MRI; Tractography; Orientation (vector space); Voxel; Tensor (intrinsic definition); Computer science; Fiber; Mathematics; Smoothness; Artificial intelligence; Biological system; Pattern recognition (psychology); Mathematical analysis; Geometry; Materials science; Magnetic resonance imaging","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":[0.002096725,0.001329624,0.001320016,0.001644266,0.0006097474,0.001642986,0.001675732,0.001273737,0.001834314],"category_scores_gemma":[0.007721471,0.0009629531,0.001538656,0.001608706,0.0009815403,0.003275197,0.001928937,0.001979652,0.00116382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009796027,"about_ca_system_score_gemma":0.001585457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006277373,"about_ca_topic_score_gemma":0.004858482,"domain_scores_codex":[0.9985526,0.0004248349,0.00009743908,0.0002834124,0.0005340843,0.0001075571],"domain_scores_gemma":[0.9968612,0.001341673,0.0004539776,0.0006909622,0.0005273294,0.0001249347],"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":[0.0002264215,0.00008064465,0.001242451,0.0002399454,0.000220779,0.0002179603,0.0002346907,0.5039247,0.02836185,0.1470795,0.003053651,0.3151175],"study_design_scores_gemma":[0.000007625531,0.00002221486,0.0002062365,0.000008859718,0.00001639489,0.00007349543,0.000009107394,0.9728184,0.002563151,0.02177889,0.002471086,0.0000245494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000837957,0.00006082538,0.9987552,0.00002038588,0.000009017369,0.000005843912,0.00001506536,0.0001945204,0.0001011335],"genre_scores_gemma":[0.09784533,0.0005160673,0.8990338,0.00002966084,0.00008632345,0.00009360853,0.000237858,0.0004352272,0.001722191],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006277373,"threshold_uncertainty_score":0.01248169,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04190387894637874,"score_gpt":0.3581343876886002,"score_spread":0.3162305087422215,"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."}}