{"id":"W2260251644","doi":"10.1002/cmr.a.21357","title":"Quantitative magnetization transfer imaging <i>made</i> easy with <i>q</i><scp>MTL</scp><i>ab</i>: Software for data simulation, analysis, and visualization","year":2015,"lang":"en","type":"article","venue":"Concepts in Magnetic Resonance Part A","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Polytechnique Montréal; Hospital for Sick Children; University of Calgary; McGill University; Montreal Neurological Institute and Hospital; University of Toronto; NeuroRx Research (Canada); Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research","keywords":"Computer science; Software; Visualization; Graphical user interface; Interface (matter); Computational science; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034453,0.0002263908,0.0004053042,0.0001709592,0.00009385747,0.00004770259,0.0001762311,0.00007930389,0.00001295847],"category_scores_gemma":[0.0006624319,0.0002070981,0.00003664075,0.0013057,0.0002414875,0.0003154649,0.00005604167,0.000114584,0.000002585526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004804472,"about_ca_system_score_gemma":0.0001365839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000243527,"about_ca_topic_score_gemma":0.00007586766,"domain_scores_codex":[0.9982406,0.00006018228,0.0004408746,0.0006683623,0.000290919,0.0002990461],"domain_scores_gemma":[0.9982411,0.0003889824,0.00009631545,0.000611091,0.000507105,0.0001554437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001400154,0.001079973,0.5449927,0.0005166987,0.0001437629,0.00004578011,0.006981086,0.0717001,0.002653795,0.03100861,0.01555353,0.3239239],"study_design_scores_gemma":[0.004814361,0.0009631445,0.02358035,0.0002736908,0.0007206117,0.00001175579,0.0009774922,0.6349551,0.0006583917,0.00180742,0.3309498,0.0002878802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0166307,0.009164853,0.9718245,0.0001900918,0.00003080982,0.00159454,0.00014597,0.0001388193,0.0002797093],"genre_scores_gemma":[0.5992798,0.001174276,0.3922562,0.001326994,0.0001762957,0.000914328,0.00314961,0.0001192897,0.0016032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5826491,"threshold_uncertainty_score":0.8445214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05067731862901596,"score_gpt":0.378106890835671,"score_spread":0.327429572206655,"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."}}