{"id":"W1906858228","doi":"10.1002/jmri.24809","title":"Whole-brain quantitative mapping of metabolites using short echo three-dimensional proton MRSI","year":2014,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Magnetic resonance spectroscopic imaging; Nuclear magnetic resonance; Echo (communications protocol); Nuclear medicine; Medicine; Radiology; Magnetic resonance imaging; Computer science; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000273735,0.0004242678,0.0002926926,0.0004315766,0.0001504509,0.0003502582,0.0001829215,0.0003144611,0.00143187],"category_scores_gemma":[0.0005458486,0.0001224751,0.0001742525,0.0003049629,0.0002692162,0.0002422425,0.0001699777,0.0001655988,0.0002449518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001332235,"about_ca_system_score_gemma":0.0002285679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001089835,"about_ca_topic_score_gemma":0.0023967,"domain_scores_codex":[0.9999224,0.00001380894,0.000005943331,0.00002646868,0.00002212874,0.000009214697],"domain_scores_gemma":[0.9998732,0.00003074524,0.00003211104,0.00001613465,0.00003499446,0.00001286675],"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":[0.001405472,0.0001366773,0.0261562,0.0003139628,0.0001933985,0.0003331768,0.000138055,0.001300027,0.8978509,0.0001825348,0.0004971464,0.07149234],"study_design_scores_gemma":[0.0001789318,0.001725046,0.6942775,0.00002629057,0.0003252697,0.004259059,0.0001481524,0.01168193,0.282581,0.001155009,0.003564963,0.00007680678],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690942,0.0009682995,0.02810127,0.00007447533,0.00001065277,0.00009255188,0.0007426805,0.0002130597,0.0007029113],"genre_scores_gemma":[0.9683948,0.0005573369,0.02942644,0.00004040578,0.00002104846,0.0001307751,0.0009547836,0.00004487539,0.0004296164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00143187,"threshold_uncertainty_score":0.004790127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03237509480691485,"score_gpt":0.3361933611676823,"score_spread":0.3038182663607675,"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."}}