{"id":"W2999285008","doi":"10.1016/j.neuroimage.2020.116533","title":"Diffusion time dependency along the human corpus callosum and exploration of age and sex differences as assessed by oscillating gradient spin-echo diffusion tensor imaging","year":2020,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Alberta","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Corpus callosum; Diffusion MRI; Diffusion; Dependency (UML); Tensor (intrinsic definition); Physics; Nuclear magnetic resonance; Spin echo; Psychology; Medicine; Magnetic resonance imaging; Neuroscience; Computer science; Artificial intelligence; Mathematics; Quantum mechanics; Radiology; 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":[],"consensus_categories":[],"category_scores_codex":[0.00008659491,0.0002081766,0.0002977058,0.00005088767,0.0003126333,0.00006187162,0.0001202026,0.00003755433,0.00001516102],"category_scores_gemma":[0.0001486104,0.0001524911,0.00004603704,0.0001672566,0.0001985301,0.0002079546,0.0001984842,0.0002819043,0.000002256973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001300014,"about_ca_system_score_gemma":0.00001303802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009592502,"about_ca_topic_score_gemma":0.000003074987,"domain_scores_codex":[0.9986385,0.00006611952,0.0003364815,0.0004849272,0.0002755708,0.0001984304],"domain_scores_gemma":[0.9991997,0.0001119172,0.0002086896,0.0002867892,0.00004964804,0.0001432137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00001448393,0.00007514766,0.07674207,0.00006205754,0.000003891833,0.00005876177,0.0004501716,5.129733e-7,0.9149665,0.0001435986,0.0002254809,0.007257287],"study_design_scores_gemma":[0.003935916,0.001651359,0.8414477,0.0005260375,0.0003792157,0.0003808387,0.0009606627,0.04558912,0.09445582,0.005766286,0.003931975,0.0009751386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906495,0.0001692848,0.003597408,0.00414803,0.00001761157,0.0006548982,0.00001609042,0.0001818172,0.0005653767],"genre_scores_gemma":[0.9973449,0.0002976319,0.001089718,0.000957525,0.00004975736,0.0000294244,0.00003068904,0.00003585036,0.0001644757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8205107,"threshold_uncertainty_score":0.6218409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06032784273529428,"score_gpt":0.3258782284872872,"score_spread":0.2655503857519929,"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."}}