{"id":"W1996893984","doi":"10.1002/cmr.b.20187","title":"Improving signal‐to‐noise ratio of hyperpolarized noble gas MR imaging at 73.5 mT using multiturn Litz wire radiofrequency receive coils","year":2011,"lang":"en","type":"article","venue":"Concepts in Magnetic Resonance Part B","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetic resonance imaging; Nuclear magnetic resonance; Signal-to-noise ratio (imaging); SIGNAL (programming language); Physics; Materials science; Medicine; Radiology; Optics; Computer science","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004069947,0.0003234636,0.0005657169,0.0001225895,0.000184604,0.00004180098,0.0005024273,0.00006762873,0.001615904],"category_scores_gemma":[0.00004335422,0.000329298,0.0001492597,0.0004114825,0.0003885018,0.0002951097,0.000246652,0.0003509158,0.00007490953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000251352,"about_ca_system_score_gemma":0.0004293714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002357534,"about_ca_topic_score_gemma":0.00001731634,"domain_scores_codex":[0.9974027,0.0002042366,0.0006780867,0.0006167063,0.0003538907,0.0007443444],"domain_scores_gemma":[0.9986466,0.0001568289,0.0002358645,0.0005598058,0.0001894148,0.0002114474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002409026,0.0002100144,0.1875462,0.0000554267,0.00002424858,0.00002512868,0.007393852,0.0001021415,0.2955739,0.002584749,0.0003695073,0.505874],"study_design_scores_gemma":[0.01007429,0.0003138318,0.01461169,0.001247796,0.0001395788,0.00001545826,0.005075801,0.6090563,0.3305924,0.01140674,0.01503219,0.002433913],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9751821,0.004093752,0.01029198,0.0000413589,0.0002202229,0.0009042277,0.0000787966,0.00003068324,0.009156849],"genre_scores_gemma":[0.9941185,0.00004567213,0.004432947,0.00005392266,0.0001926555,0.00008530983,0.00001559914,0.00004789607,0.00100754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6089541,"threshold_uncertainty_score":0.9999159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02864304266242395,"score_gpt":0.2835974044269123,"score_spread":0.2549543617644883,"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."}}