{"id":"W4212817125","doi":"10.3389/fninf.2022.777853","title":"Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","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; NIH Blueprint for Neuroscience Research; Canada Research Chairs; Canada First Research Excellence Fund; Canada Foundation for Innovation; Compute Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Cluster analysis; Human Connectome Project; Tractography; Reliability (semiconductor); Pattern recognition (psychology); Computer science; Artificial intelligence; White matter; Centroid; Neuroscience; Psychology; Functional connectivity; Medicine; Magnetic resonance imaging; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004063171,0.0000944229,0.0002283244,0.0002404364,0.0001709771,0.00001215326,0.0001712795,0.0000233639,0.000001896392],"category_scores_gemma":[0.00008081322,0.00008218521,0.00008004493,0.000469153,0.00007325311,0.0001411641,0.00005861653,0.0003829671,4.269823e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008205968,"about_ca_system_score_gemma":0.00005986873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001390497,"about_ca_topic_score_gemma":0.000003289481,"domain_scores_codex":[0.998885,0.00004043595,0.0005787281,0.0001300066,0.0001732866,0.0001925088],"domain_scores_gemma":[0.9991927,0.0001234464,0.0002252042,0.0003939119,0.00003168482,0.00003306723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002550016,0.002147391,0.7909557,0.004480429,0.0000196082,0.00001968193,0.004563792,0.1434929,0.0008734072,0.0005226932,0.01922808,0.03114639],"study_design_scores_gemma":[0.004327912,0.001192373,0.0729748,0.000215513,0.00002984776,0.0000171291,0.00343516,0.8963255,0.0004650436,0.00243041,0.01834707,0.0002392984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8040864,0.0000341522,0.1914836,0.001837515,0.0002342214,0.001929978,0.00003351036,0.00007991637,0.000280724],"genre_scores_gemma":[0.8919086,0.00001090409,0.1070423,0.0007415282,0.00001270703,0.0002374593,0.00002820694,0.00001564292,0.000002676029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7528326,"threshold_uncertainty_score":0.3351416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05859852119323517,"score_gpt":0.3744245438312235,"score_spread":0.3158260226379883,"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."}}