{"id":"W1599086055","doi":"10.1002/nbm.3193","title":"Improving the spectral resolution and spectral fitting of <sup>1</sup>H MRSI data from human calf muscle by the SPREAD technique","year":2014,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Columbia College","funders":"","keywords":"Deconvolution; Spectral resolution; Nuclear magnetic resonance; Spectral line; Resolution (logic); Magnetic resonance spectroscopic imaging; Physics; Materials science; Magnetic resonance imaging; Optics; Computer science; Medicine; Artificial intelligence; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008778776,0.0001540133,0.0002768991,0.00008295768,0.0001512448,0.000009693245,0.0003754051,0.0001032808,0.00003822554],"category_scores_gemma":[0.000182859,0.0000898342,0.00002957002,0.0003167845,0.0004589657,0.0000754931,0.0002001104,0.0003899923,0.000001379379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006225798,"about_ca_system_score_gemma":0.00002229736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002606386,"about_ca_topic_score_gemma":0.0001086818,"domain_scores_codex":[0.9986548,0.00005516115,0.0004191629,0.0003806028,0.0002292411,0.0002610733],"domain_scores_gemma":[0.9984515,0.0002220106,0.0001704522,0.001051911,0.00003539312,0.00006869707],"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":[0.0000538867,0.0001809207,0.002625046,0.00006942567,0.00001696566,0.000005387353,0.0005327814,0.0000170287,0.9440807,0.002382059,0.01366012,0.03637566],"study_design_scores_gemma":[0.01074868,0.003683729,0.1130673,0.003233497,0.0009490945,0.0003495213,0.006065414,0.2247147,0.3066496,0.03411074,0.2949802,0.001447497],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7365965,0.001727773,0.2386589,0.01728369,0.00004288635,0.00255647,0.0002747573,0.0002483081,0.002610646],"genre_scores_gemma":[0.9799408,0.0001180079,0.01855577,0.000381489,0.000431456,0.00006947449,0.00035894,0.00002409876,0.0001199242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6374311,"threshold_uncertainty_score":0.3940093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693751142288922,"score_gpt":0.3189679841437223,"score_spread":0.2920304727208332,"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."}}