{"id":"W2949229906","doi":"10.1016/j.jmr.2019.06.005","title":"TRASE 1D sequence performance in imperfect<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si125.svg\"><mml:mrow><mml:msub><mml:mrow><mml:mi>B</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>fields","year":2019,"lang":"lv","type":"article","venue":"Journal of Magnetic Resonance","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Flip angle; Pulse sequence; Physics; Amplitude; Nuclear magnetic resonance; Algorithm; Computer science; Computational physics; Optics; Magnetic resonance imaging","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":[0.001963709,0.001115989,0.0007338001,0.001425909,0.0008526577,0.002662472,0.0006855142,0.001575019,0.04260042],"category_scores_gemma":[0.01191644,0.0005659342,0.0003997655,0.0009681844,0.0005134813,0.002275621,0.001235364,0.001227755,0.01372867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006187314,"about_ca_system_score_gemma":0.001251944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00602053,"about_ca_topic_score_gemma":0.009456605,"domain_scores_codex":[0.999339,0.0001195734,0.00009536467,0.0001328193,0.0002223624,0.0000906725],"domain_scores_gemma":[0.9964449,0.001534795,0.0001533552,0.0004111513,0.001249884,0.0002060256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0119268,0.0008192137,0.00877333,0.002495111,0.0003656551,0.001420922,0.001621509,0.1056622,0.194234,0.01611692,0.1300351,0.5265291],"study_design_scores_gemma":[0.0004193228,0.003578988,0.02727557,0.0006739887,0.0002803436,0.004476865,0.001558137,0.463241,0.3478302,0.01940824,0.130473,0.0007844074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3999543,0.00730942,0.4234558,0.00350908,0.003169725,0.0007433243,0.0198512,0.03762861,0.1043787],"genre_scores_gemma":[0.6462184,0.002580706,0.262886,0.0009677641,0.0002239636,0.0005273968,0.02319677,0.008739698,0.05465934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04260042,"threshold_uncertainty_score":0.1425126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01773708155883883,"score_gpt":0.2591295803351802,"score_spread":0.2413924987763414,"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."}}