{"id":"W2054655404","doi":"10.1002/mrm.23071","title":"Iterative optimization method for design of quantitative magnetization transfer imaging experiments","year":2011,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University; Montreal Neurological Institute and Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Magnetization transfer; Computer science; Magnetization; Nuclear magnetic resonance; Iterative method; Materials science; Physics; Algorithm; Magnetic resonance imaging; Radiology; Magnetic field; Medicine","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":[0.00468551,0.001739911,0.001566109,0.001039951,0.00050366,0.0008876641,0.001049728,0.001259508,0.00358402],"category_scores_gemma":[0.006638298,0.0009604178,0.001267019,0.0007413632,0.0008244853,0.0005447062,0.001103443,0.001488317,0.000819766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008707967,"about_ca_system_score_gemma":0.002097714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001860445,"about_ca_topic_score_gemma":0.001818373,"domain_scores_codex":[0.9978908,0.001102619,0.00009153369,0.0002061229,0.0006094456,0.00009962769],"domain_scores_gemma":[0.9967992,0.002242488,0.0002000228,0.0001142572,0.0006010642,0.00004293136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000880855,0.00005922456,0.000157727,0.000257575,0.00006591483,0.00004943699,0.00008491086,0.9395229,0.005376275,0.008181231,0.0005015795,0.04565523],"study_design_scores_gemma":[0.00003063963,0.0001272299,0.00007756109,0.00001743738,0.00001879942,0.00001503642,0.000009844728,0.9931918,0.001758726,0.002744485,0.001996342,0.00001207025],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001067616,0.00006219024,0.9979268,0.00001506012,0.00000821213,0.00006965402,0.00001118938,0.0001467165,0.0006925075],"genre_scores_gemma":[0.0597752,0.0001361898,0.9372541,0.00004593335,0.00001293087,0.001371324,0.00009026386,0.000136433,0.001177615],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00468551,"threshold_uncertainty_score":0.02477962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08972345850548907,"score_gpt":0.3972685288846809,"score_spread":0.3075450703791918,"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."}}