{"id":"W3197146939","doi":"10.3389/fnins.2021.716538","title":"Tractography in Curvilinear Coordinates","year":2021,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"McDonnell Center for Systems Neuroscience; National Institutes of Health; Canadian Open Neuroscience Platform; Natural Sciences and Engineering Research Council of Canada; NIH Blueprint for Neuroscience Research; Canada Research Chairs","keywords":"Curvilinear coordinates; Bipolar coordinates; Orthogonal coordinates; Log-polar coordinates; Cartesian coordinate system; Parabolic coordinates; Context (archaeology); Action-angle coordinates; Spherical coordinate system; Coordinate system; Tractography; Cartesian tensor; Parallel coordinates; Computer science; Generalized coordinates; Spatial reference system; Polar coordinate system; Visualization; Artificial intelligence; Mathematics; Geometry; Mathematical analysis; Diffusion MRI; Data visualization; Geology; Tensor field","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":[0.000681906,0.0005071017,0.0003070889,0.0008976168,0.000351569,0.001269614,0.0004312887,0.0005398652,0.004916866],"category_scores_gemma":[0.003481194,0.0002879482,0.0006063354,0.001187602,0.0008432596,0.001282725,0.0008551161,0.0006613198,0.001322383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000590294,"about_ca_system_score_gemma":0.000754466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00631616,"about_ca_topic_score_gemma":0.006400602,"domain_scores_codex":[0.9997192,0.0000906207,0.00002523502,0.00008855229,0.00005402046,0.00002234914],"domain_scores_gemma":[0.9986431,0.000538943,0.0002673611,0.0003000482,0.0001958646,0.00005466822],"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":[0.0001794394,0.00002370312,0.004777152,0.0002068303,0.0001145306,0.0006221642,0.0005736824,0.5128031,0.04839248,0.3461304,0.005444528,0.080732],"study_design_scores_gemma":[0.0000224249,0.00006585736,0.003057064,0.00003250945,0.00002826976,0.0004369203,0.00009166893,0.8573806,0.01214175,0.1066576,0.02003822,0.00004712848],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02165956,0.0001986493,0.9737025,0.0001632692,0.00004338309,0.00002898383,0.0003507512,0.0007187636,0.003134037],"genre_scores_gemma":[0.3109611,0.0009049909,0.6800703,0.00009736623,0.00005130346,0.0001238704,0.0006367416,0.000755694,0.006398674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00631616,"threshold_uncertainty_score":0.01644862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0487326283880001,"score_gpt":0.3453726620877228,"score_spread":0.2966400336997227,"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."}}