{"id":"W2004658690","doi":"10.1021/ja044032o","title":"High-Resolution Four-Dimensional <sup>1</sup>H−<sup>13</sup>C NOE Spectroscopy using Methyl-TROSY, Sparse Data Acquisition, and Multidimensional Decomposition","year":2005,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Photosynthetic Processes and Mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":163,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Chemistry; Resolution (logic); Deuterium; Spectroscopy; Sensitivity (control systems); Spectral line; Spectral resolution; Sampling (signal processing); Decomposition; Relaxation (psychology); Nuclear magnetic resonance; Analytical Chemistry (journal); Two-dimensional nuclear magnetic resonance spectroscopy; Chromatography; Physics; Atomic physics; Stereochemistry; Artificial intelligence; Optics; Computer science; Detector","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":[0.0005053685,0.0002629437,0.0003878043,0.00002463439,0.0002374664,0.00003761773,0.0004963176,0.0001413577,0.00002876623],"category_scores_gemma":[0.0001010118,0.0001989879,0.0003018887,0.0001629289,0.0003667138,0.0000413287,0.0006022999,0.0003507485,0.000002354794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281898,"about_ca_system_score_gemma":0.0001712999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005567536,"about_ca_topic_score_gemma":0.000001183181,"domain_scores_codex":[0.9981422,0.0001011423,0.0004833554,0.000426355,0.0004710922,0.0003758091],"domain_scores_gemma":[0.998456,0.00006081252,0.000595857,0.0004779719,0.0002051802,0.0002041766],"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":[0.0002387433,0.000152943,0.00007740394,0.0000106808,0.0001595376,0.000002067686,0.00006104661,0.01006214,0.9864479,0.00001464877,0.002387699,0.0003851876],"study_design_scores_gemma":[0.0009296859,0.0001808573,0.0001287582,0.00006137574,0.0001762338,0.0005357257,0.0001504982,0.09042946,0.9053553,0.0002567581,0.001555831,0.0002395523],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874598,0.0003616264,0.01027624,0.00159863,0.00006685365,0.0001185428,0.0001032251,0.000008914766,0.000006108956],"genre_scores_gemma":[0.8893056,0.0002448845,0.1075476,0.001883134,0.0008937912,0.000002081827,0.00007968128,0.00002865425,0.00001463176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09815427,"threshold_uncertainty_score":0.8114491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593167563188581,"score_gpt":0.2795627370046408,"score_spread":0.263631061372755,"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."}}