{"id":"W2029665903","doi":"10.1016/j.media.2011.01.005","title":"Extracting skeletal muscle fiber fields from noisy diffusion tensor data","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Canada Research Chairs","keywords":"Smoothing; Diffusion MRI; Noise (video); Tensor (intrinsic definition); Noise reduction; Artificial intelligence; Pattern recognition (psychology); Mathematics; Fiber; Synthetic data; SIGNAL (programming language); Computer science; Algorithm; Computer vision; Geometry","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002047382,0.0001373666,0.0003405049,0.0001272695,0.00009782246,0.00001573698,0.0004137059,0.0001137,0.01153279],"category_scores_gemma":[0.0006775911,0.000108843,0.0001743595,0.0005479286,0.0001243307,0.0001689643,0.00034149,0.0004188323,0.0001414257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000145225,"about_ca_system_score_gemma":0.00003070076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007747508,"about_ca_topic_score_gemma":0.0000467189,"domain_scores_codex":[0.9984352,0.000033686,0.000322838,0.0005335751,0.0004528715,0.0002218565],"domain_scores_gemma":[0.9980755,0.0001509037,0.00009988077,0.001330689,0.00006874718,0.0002742405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00007714307,0.002537092,0.06217499,0.00006500159,0.001717495,0.001900844,0.0007125125,0.000001602748,0.01990655,0.00009712546,0.03363788,0.8771718],"study_design_scores_gemma":[0.00227521,0.0002218113,0.6173186,0.0002267385,0.01256114,0.00009565859,0.0004159421,0.1454955,0.005877181,0.002114331,0.2123351,0.001062756],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3829389,0.0002043261,0.5955779,0.006789516,0.00005413892,0.0003168467,0.0001253437,0.0005494533,0.01344363],"genre_scores_gemma":[0.8841178,0.000165694,0.1113906,0.001867025,0.0002774204,0.00002351869,0.000747007,0.00002929772,0.001381579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.876109,"threshold_uncertainty_score":0.9893708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1199979832209549,"score_gpt":0.3793596533652879,"score_spread":0.259361670144333,"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."}}