{"id":"W2054335197","doi":"10.1002/mrm.22487","title":"Transverse relaxometry with stimulated echo compensation","year":2010,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research; Fondation pour la Recherche Médicale","keywords":"Multislice; Relaxometry; Spin echo; Nuclear magnetic resonance; Pulse sequence; SIGNAL (programming language); Physics; Coherence (philosophical gambling strategy); Adiabatic process; Transverse plane; Computer science; Magnetic resonance imaging; Medicine; 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":[0.0006556798,0.000855242,0.0006280584,0.0002644831,0.0002344863,0.0005410849,0.001271259,0.001043782,0.001977799],"category_scores_gemma":[0.001633696,0.0004559231,0.0007856906,0.0004699438,0.0002855204,0.0008357367,0.0004080956,0.0006488824,0.002336001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005617986,"about_ca_system_score_gemma":0.0009547776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00208509,"about_ca_topic_score_gemma":0.002079692,"domain_scores_codex":[0.9996637,0.00007255374,0.00001448952,0.0001221603,0.0001052845,0.00002179988],"domain_scores_gemma":[0.9996887,0.00007939956,0.00005010706,0.0000639242,0.0001026737,0.0000151424],"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":[0.0002868659,0.0001199843,0.001914963,0.0002832884,0.0001301272,0.00043833,0.0001904265,0.690222,0.1862637,0.01666876,0.003696065,0.09978537],"study_design_scores_gemma":[0.000008315743,0.00009157394,0.0003755,0.00001205539,0.00002540364,0.0002261869,0.000007854101,0.9759113,0.01503433,0.002201639,0.00607283,0.00003292468],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01094801,0.0001410919,0.9868305,0.0001064175,0.00004045917,0.00004065868,0.00015781,0.0008373391,0.000897784],"genre_scores_gemma":[0.4177461,0.00151435,0.5561865,0.0002870267,0.00007055775,0.0005115317,0.001539178,0.00109415,0.02105074],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00208509,"threshold_uncertainty_score":0.006616414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01449246453366811,"score_gpt":0.3063822027070628,"score_spread":0.2918897381733947,"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."}}