{"id":"W2796316204","doi":"10.1101/277038","title":"Voxel-wise T <sub>2</sub> relaxometry of Normal Pediatric Brain Development in 326 healthy infants and toddlers","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Voxel; Weighting; Range (aeronautics); Relaxometry; Artificial intelligence; Development (topology); Brain development; Pattern recognition (psychology); Mathematics; Nuclear medicine; Computer science; Medicine; Psychology; Magnetic resonance imaging; Radiology; Neuroscience; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004234149,0.0004140224,0.0003859151,0.0009437262,0.0002738676,0.0002859549,0.0002086148,0.0002817193,0.0007073012],"category_scores_gemma":[0.001569321,0.0002972505,0.000179492,0.0004518562,0.0003843546,0.000181072,0.0003136209,0.0001472738,0.0001934347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737697,"about_ca_system_score_gemma":0.0001917234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01243877,"about_ca_topic_score_gemma":0.006601866,"domain_scores_codex":[0.9998341,0.00003067486,0.00001943705,0.00005307055,0.00003273648,0.00002998452],"domain_scores_gemma":[0.9996847,0.00008171709,0.00008036529,0.00005041132,0.00006568307,0.00003719048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001497098,0.00009182572,0.9393297,0.00008092702,0.0000902461,0.002517847,0.001700649,0.0005972544,0.04067317,0.0001077912,0.0002572697,0.01305611],"study_design_scores_gemma":[0.000010822,0.0001974334,0.994818,0.000003185181,0.00002246553,0.001235761,0.0003023542,0.0002425931,0.002848746,0.00002281975,0.0002897384,0.000006011425],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995658,0.00003958691,0.0001062948,0.000003021623,3.677066e-7,0.000002874298,0.0002104875,0.000007886154,0.00006359269],"genre_scores_gemma":[0.9992235,0.00004726916,0.0002875657,0.000002683136,0.000001023873,0.000007852173,0.000325026,0.000007487231,0.00009756659],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01243877,"threshold_uncertainty_score":0.02473277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02132484984811435,"score_gpt":0.2306291799550157,"score_spread":0.2093043301069013,"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."}}