{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity","insufficient_payload"],"category_scores_codex":[0.00256648,0.0009077734,0.0004983332,0.0006738161,0.0009823695,0.000753735,0.002451388,0.002258716,0.6240487],"category_scores_gemma":[0.001538332,0.001658394,0.001644554,0.00153309,0.001567787,0.001647983,0.00124648,0.003011398,0.001758179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003548759,"about_ca_system_score_gemma":0.002259993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009063409,"about_ca_topic_score_gemma":0.0007737996,"domain_scores_codex":[0.9899597,0.0003920466,0.002636963,0.001639406,0.002795381,0.002576505],"domain_scores_gemma":[0.9915162,0.001457806,0.002828416,0.002597216,0.0003066187,0.001293685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003857036,0.0008440852,0.0001517383,0.002751541,0.0007659193,0.004228262,0.003144959,0.002997831,0.01481397,0.2532372,0.6809332,0.0322742],"study_design_scores_gemma":[0.003062686,0.004914902,0.000887724,0.003586676,0.001062802,0.004138026,0.001509181,0.0329538,0.9236934,0.0001068965,0.02252092,0.001562954],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5073954,0.006580382,0.001175359,0.001583298,0.001655186,0.00006869603,0.0001645484,0.0001334187,0.4812437],"genre_scores_gemma":[0.9716192,0.01254284,0.00783176,0.002420623,0.002317153,0.0009443286,0.0003008498,0.0006578393,0.001365443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9088795,"threshold_uncertainty_score":0.9992887,"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."}}