{"id":"W3093873509","doi":"10.3389/fnagi.2020.585218","title":"Detection of Cerebrovascular Loss in the Normal Aging C57BL/6 Mouse Brain Using in vivo Contrast-Enhanced Magnetic Resonance Angiography","year":2020,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Research Infrastructure Programs, National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; National Center for Advancing Translational Sciences; Materials Research Science and Engineering Center, Harvard University; Division of Materials Research; Georgia Clinical and Translational Science Alliance; National Science Foundation; Compute Canada; American Health Assistance Foundation; National Cancer Institute; National Institutes of Health","keywords":"Magnetic resonance angiography; Magnetic resonance imaging; In vivo; Contrast (vision); Pathology; Angiography; Medicine; Nuclear medicine; Radiology; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008750875,0.0009083836,0.0004374024,0.001591892,0.000292386,0.0005010138,0.0003954006,0.0006408949,0.001631023],"category_scores_gemma":[0.0004900608,0.0003320457,0.0003348208,0.0003485052,0.000509826,0.000550301,0.00027689,0.001156659,0.0004050341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002231664,"about_ca_system_score_gemma":0.0002154636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008003367,"about_ca_topic_score_gemma":0.001510174,"domain_scores_codex":[0.9996684,0.00003614879,0.00002957117,0.0001208084,0.00008427553,0.00006091566],"domain_scores_gemma":[0.9994313,0.00007651483,0.0001745789,0.00006772699,0.0001227377,0.0001272194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001413826,0.00005719602,0.0004047602,0.00004094603,0.00001398022,0.000132081,0.00004507334,0.00003021452,0.9974138,0.000135267,0.00005450135,0.001530722],"study_design_scores_gemma":[0.00003206048,0.0009699911,0.02944563,0.00004343922,0.0001235299,0.00164243,0.00009557016,0.00165805,0.9634933,0.0003516482,0.002116358,0.00002796757],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9604947,0.003155246,0.03221827,0.0001843533,0.0001495107,0.00008291628,0.0009486406,0.0006533019,0.002112931],"genre_scores_gemma":[0.9493946,0.004234139,0.03653393,0.0002057817,0.0000706049,0.0003015688,0.00107579,0.0002353221,0.007948152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001631023,"threshold_uncertainty_score":0.005456328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420625213105669,"score_gpt":0.2627975097789554,"score_spread":0.2485912576478987,"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."}}