{"id":"W2041750706","doi":"10.1002/jmri.21079","title":"Investigating exchange and multicomponent relaxation in fully‐balanced steady‐state free precession imaging","year":2008,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"Canadian Institutes of Health Research","keywords":"Steady-state free precession imaging; Relaxation (psychology); Precession; Nuclear magnetic resonance; SIGNAL (programming language); T2 relaxation; Pulse (music); Magnetization; Relaxometry; Chemistry; Physics; Computational physics; Statistical physics; Magnetic resonance imaging; Optics; Spin echo; Condensed matter physics; Computer science; Magnetic field; Quantum mechanics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003525793,0.0001421323,0.0002917921,0.0002051016,0.0001021205,0.0000175962,0.0001188092,0.00002494959,0.00001365933],"category_scores_gemma":[0.0002637834,0.000122931,0.00004621557,0.0002172855,0.0001439035,0.0002993984,0.00007621708,0.0003718995,0.000001010762],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000103322,"about_ca_system_score_gemma":0.00006089787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003375487,"about_ca_topic_score_gemma":0.000004309676,"domain_scores_codex":[0.9986233,0.00004541448,0.0006016463,0.000195948,0.0003008018,0.0002329012],"domain_scores_gemma":[0.9989648,0.00008566139,0.0004401059,0.0002226199,0.000155447,0.0001313702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00008993704,0.0001209148,0.3059523,0.0000834243,0.00000157615,0.0002288393,0.001859263,0.0001144028,0.07503276,0.00003017245,0.0009387668,0.6155477],"study_design_scores_gemma":[0.004873277,0.0001914842,0.9275757,0.002808628,0.00003151705,0.002587186,0.0004418255,0.04216971,0.005146232,0.0030571,0.01084249,0.0002748698],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9519101,0.03065605,0.01186485,0.004558817,0.00006521313,0.0003836615,0.00000342399,0.00004239843,0.0005155217],"genre_scores_gemma":[0.8614835,0.004992125,0.1328067,0.00038085,0.0001102903,0.00002152415,0.000002188951,0.00002695439,0.0001759778],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6216233,"threshold_uncertainty_score":0.5012982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857519557009936,"score_gpt":0.297277221874325,"score_spread":0.2787020263042256,"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."}}