{"id":"W2894251763","doi":"10.1002/hbm.24337","title":"The morphology of the human cerebrovascular system","year":2018,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":141,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Canada Foundation for Innovation; Réseau en Bio-Imagerie du Quebec; Université de Sherbrooke","keywords":"Brain morphometry; Human brain; Precuneus; Cuneus; White matter; Neuroscience; Medicine; Computer science; Magnetic resonance imaging; Radiology; Psychology; Functional magnetic resonance imaging","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":[0.0002930893,0.0001339536,0.0001494088,0.001082925,0.0002324846,0.0008150141,0.0001623375,0.0002113593,0.0009442793],"category_scores_gemma":[0.001358149,0.0001813975,0.0001060458,0.0006772872,0.0003965226,0.0005102978,0.0002367917,0.0001164624,0.0002367652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001937447,"about_ca_system_score_gemma":0.0002859004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003084852,"about_ca_topic_score_gemma":0.004858273,"domain_scores_codex":[0.9998448,0.00003998026,0.000009737661,0.00005662413,0.00003804491,0.00001076788],"domain_scores_gemma":[0.9997179,0.00009598186,0.0000679146,0.00004751469,0.00005262322,0.00001799325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005769664,0.00007786418,0.1960583,0.0005487004,0.0003934746,0.001859468,0.00225089,0.01812716,0.3473911,0.00995078,0.006232266,0.416533],"study_design_scores_gemma":[0.000009862268,0.0001304116,0.9526056,0.00006295352,0.00008246394,0.00516703,0.0003035275,0.01644895,0.0119124,0.006772352,0.006451555,0.00005285337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9527439,0.001799648,0.03909631,0.000234065,0.00001617714,0.00003886379,0.001214347,0.0002311773,0.004625546],"genre_scores_gemma":[0.9822167,0.001194244,0.01522378,0.00004294323,0.00002123248,0.00003200156,0.000483344,0.00004320139,0.0007426944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003084852,"threshold_uncertainty_score":0.006133795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0526958584302908,"score_gpt":0.2700998110995139,"score_spread":0.2174039526692231,"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."}}