{"id":"W2114539524","doi":"10.1186/s12968-015-0187-0","title":"Saturation pulse design for quantitative myocardial T1 mapping","year":2015,"lang":"en","type":"article","venue":"Journal of Cardiovascular Magnetic Resonance","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"NHLBI Division of Intramural Research; National Cancer Institute; U.S. Department of Health and Human Services; National Institutes of Health; Canadian Institutes of Health Research; National Heart, Lung, and Blood Institute; Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Saturation (graph theory); Adiabatic process; Physics; Residual; Pulse (music); Nuclear magnetic resonance; Optics; Materials science; Analytical Chemistry (journal); Computational physics; Mathematics; Chemistry; Algorithm","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":[0.0009414069,0.0004588983,0.0001704572,0.0002240123,0.0001662741,0.0003030618,0.000508667,0.0004032545,0.002053047],"category_scores_gemma":[0.0009834564,0.0002635201,0.0001452348,0.0002456954,0.0002944865,0.0003475891,0.000306211,0.0003421537,0.000611601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005400231,"about_ca_system_score_gemma":0.0005779138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003716433,"about_ca_topic_score_gemma":0.0006409862,"domain_scores_codex":[0.9998272,0.00005783785,0.00001001649,0.00002928537,0.0000594782,0.00001623176],"domain_scores_gemma":[0.9995679,0.0001693874,0.00006783057,0.00002313965,0.0001475244,0.0000242483],"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":[0.0006293198,0.00007861394,0.0008571949,0.0003925849,0.00003335546,0.0001175317,0.00007681145,0.02940407,0.880134,0.002545076,0.0008763402,0.08485514],"study_design_scores_gemma":[0.0001605995,0.001780037,0.002479455,0.00005253101,0.0001044935,0.0005489541,0.00003804607,0.2193687,0.7587371,0.003416458,0.01324849,0.00006519622],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06504536,0.0006538939,0.9319495,0.0001380458,0.00002364323,0.0001739398,0.00009657839,0.0005318082,0.001387191],"genre_scores_gemma":[0.3453952,0.0007389167,0.6510379,0.0002080083,0.00003484249,0.0005752324,0.0002142114,0.0001773548,0.001618367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002053047,"threshold_uncertainty_score":0.006868124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07708459142051367,"score_gpt":0.3195014379825839,"score_spread":0.2424168465620702,"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."}}