{"id":"W2083313302","doi":"10.1007/s10858-009-9321-3","title":"Measuring 13Cβ chemical shifts of invisible excited states in proteins by relaxation dispersion NMR spectroscopy","year":2009,"lang":"en","type":"article","venue":"Journal of Biomolecular NMR","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Canada Research Chairs; Hospital for Sick Children","funders":"Vetenskapsrådet; Canada Research Chairs","keywords":"Chemistry; Dispersion (optics); Relaxation (psychology); Chemical shift; Excited state; Nuclear magnetic resonance spectroscopy; Carbon-13 NMR; Spectroscopy; Analytical Chemistry (journal); Ligand (biochemistry); Crystallography; Physical chemistry; Stereochemistry; Chromatography; Biochemistry; Atomic physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002763852,0.0001572959,0.0002453745,0.0001411894,0.00002457992,0.00001947564,0.0002161447,0.0001817084,0.00000456927],"category_scores_gemma":[0.0001231528,0.0001389971,0.0001404575,0.0002296511,0.00005059183,0.00001496268,0.00003951154,0.0001839888,6.369566e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005087994,"about_ca_system_score_gemma":0.00007447077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001451331,"about_ca_topic_score_gemma":0.000004425878,"domain_scores_codex":[0.9987351,0.00006680198,0.0004913969,0.0001917141,0.0002992508,0.0002157142],"domain_scores_gemma":[0.9991544,0.000005506682,0.0004088431,0.0002059841,0.0001350339,0.00009024212],"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":[0.0002927897,0.0001134724,0.0006326899,0.00001879819,0.00003063482,0.00001950796,0.00005280809,0.0001442887,0.997052,0.00003091428,0.0002091964,0.00140297],"study_design_scores_gemma":[0.0009730306,0.000738673,0.001579825,0.00009576458,0.00001837353,0.00003921055,0.00002839512,0.000423457,0.994381,0.001130802,0.0004395523,0.0001518864],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9588103,0.001818622,0.03873315,0.0002995645,0.00003478352,0.0001737395,0.000009006581,0.000003758025,0.0001170489],"genre_scores_gemma":[0.9945806,0.0002856379,0.00482864,0.0001439828,0.00007730065,0.000001425902,0.00005502913,0.00001474634,0.00001259045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03577032,"threshold_uncertainty_score":0.5668136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005957944081707687,"score_gpt":0.2329774162415838,"score_spread":0.2270194721598761,"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."}}