{"id":"W3109249627","doi":"10.1101/2020.11.24.396549","title":"Processing the diffusion-weighted magnetic resonance imaging of the PING dataset","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Sherbrooke; McGill University Health Centre","funders":"Canada First Research Excellence Fund; Université de Sherbrooke; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Diffusion MRI; White matter; Ping (video games); Computer science; Artificial intelligence; Magnetic resonance imaging; Neuroimaging; Visualization; Pattern recognition (psychology); Neuroscience; Medicine; Psychology; Radiology","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":[0.00141753,0.001359422,0.0007317328,0.001986925,0.0004785894,0.001214292,0.001027412,0.001051418,0.005270143],"category_scores_gemma":[0.005028225,0.0002691857,0.001142101,0.001316059,0.0004253312,0.0004463632,0.001477446,0.0007989948,0.005356699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004618384,"about_ca_system_score_gemma":0.001189358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00648292,"about_ca_topic_score_gemma":0.01055565,"domain_scores_codex":[0.9992296,0.0001727136,0.00007257959,0.0002589852,0.0001575943,0.0001086556],"domain_scores_gemma":[0.9990923,0.0002114876,0.00005747796,0.0003615021,0.0002182072,0.00005896493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001649208,0.0006087784,0.03842501,0.001856131,0.001159375,0.002859924,0.0005581137,0.02135359,0.02422067,0.004069818,0.671855,0.2313844],"study_design_scores_gemma":[0.0007370301,0.0006517904,0.1461624,0.0005897445,0.0005189163,0.005854663,0.0009950149,0.0587503,0.03660806,0.01883117,0.7299767,0.0003242257],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1987838,0.002118444,0.03213548,0.001573174,0.0007690208,0.0009583196,0.7429035,0.01306819,0.007690018],"genre_scores_gemma":[0.103663,0.0004790317,0.03775295,0.0002284633,0.0001309603,0.0008840776,0.852423,0.0007653302,0.003673287],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.00648292,"threshold_uncertainty_score":0.0176304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03162999053975384,"score_gpt":0.2804629591585016,"score_spread":0.2488329686187477,"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."}}