{"id":"W3110641764","doi":"10.1101/2020.12.03.408567","title":"MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"National Institutes of Health; Vanderbilt University; National Science Foundation","keywords":"Diffusion MRI; Connectomics; Fractional anisotropy; Connectome; Pattern recognition (psychology); Artificial intelligence; Magnetic resonance imaging; Orientation (vector space); Nuclear magnetic resonance; Computer science; Mathematics; Psychology; Medicine; Neuroscience; Physics; Radiology; Functional connectivity","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":[0.002794997,0.001086564,0.0007888821,0.001971358,0.0005193774,0.0007994674,0.001028305,0.0007308529,0.001675518],"category_scores_gemma":[0.005742827,0.0003844568,0.0008845225,0.001138252,0.0004941313,0.0007145229,0.001371929,0.0008026727,0.0004933371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002914048,"about_ca_system_score_gemma":0.0008621291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002611645,"about_ca_topic_score_gemma":0.00813882,"domain_scores_codex":[0.9990658,0.0002579765,0.00008742722,0.000370256,0.0001608735,0.00005766228],"domain_scores_gemma":[0.9969503,0.0009309269,0.0007552397,0.0008909266,0.0003219435,0.0001506446],"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":[0.004913899,0.001428842,0.1705441,0.001280261,0.003944758,0.001186075,0.001028486,0.08813615,0.3307246,0.005299564,0.06533351,0.3261798],"study_design_scores_gemma":[0.0006412978,0.001789634,0.4306143,0.0001285044,0.000697541,0.002970341,0.0003508912,0.4394001,0.08626611,0.01033644,0.02631406,0.0004907379],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6624054,0.0007339661,0.2911135,0.0002641678,0.0001268486,0.0005914752,0.03271463,0.01094018,0.001109887],"genre_scores_gemma":[0.6430848,0.0002549039,0.2936022,0.0001318769,0.0001222709,0.002175338,0.05740014,0.001802407,0.001426066],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002794997,"threshold_uncertainty_score":0.01478153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137716795229804,"score_gpt":0.2888891434856524,"score_spread":0.2575119755333544,"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."}}