{"id":"W2097840523","doi":"10.1109/hisb.2011.19","title":"Consistent Information Content Estimation for Diffusion Tensor MR Images","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Estimator; Diffusion MRI; Entropy estimation; Computer science; Artificial intelligence; Entropy (arrow of time); Tensor (intrinsic definition); Curse of dimensionality; Pattern recognition (psychology); Segmentation; Context (archaeology); Image segmentation; Image registration; Thresholding; Mathematics; Computer vision; Image (mathematics); Statistics","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":[0.00420415,0.0007387189,0.001099215,0.002853849,0.0004143374,0.001547746,0.001413605,0.00129699,0.0006652698],"category_scores_gemma":[0.02231665,0.0006315311,0.000616158,0.001751299,0.001353779,0.003847047,0.001584391,0.001399283,0.0004853954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007268501,"about_ca_system_score_gemma":0.0007581103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009984144,"about_ca_topic_score_gemma":0.001127406,"domain_scores_codex":[0.9982613,0.0005864543,0.0001318412,0.0003332288,0.0006137122,0.00007353924],"domain_scores_gemma":[0.9911016,0.005373622,0.001020905,0.001168162,0.001178546,0.0001571483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003478,0.0001596785,0.005844331,0.0004075987,0.0002566373,0.0001992987,0.0003141961,0.4329651,0.04597425,0.120246,0.003371303,0.3899138],"study_design_scores_gemma":[0.00001482418,0.00005084702,0.001124988,0.00002439851,0.00002240543,0.00007980941,0.00001716301,0.9293653,0.01251712,0.05560867,0.001132043,0.00004236706],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005074107,0.0001685046,0.9943715,0.00006357036,0.00001029484,0.00001322336,0.00004088851,0.000136443,0.0001214198],"genre_scores_gemma":[0.2524649,0.0007072798,0.7445593,0.0001732245,0.0001454696,0.0001733038,0.0006769685,0.0002831989,0.0008164532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00420415,"threshold_uncertainty_score":0.02223396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1997058112435429,"score_gpt":0.3487328681061393,"score_spread":0.1490270568625964,"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."}}