{"id":"W4300817772","doi":"10.1007/s10858-022-00403-2","title":"Optimizing frequency sampling in CEST experiments","year":2022,"lang":"en","type":"article","venue":"Journal of Biomolecular NMR","topic":"Electron Spin Resonance Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Biotechnology and Biological Sciences Research Council","keywords":"A priori and a posteriori; Sampling (signal processing); Spins; Chemistry; Frequency domain; Biomolecule; Statistical physics; Biological system; Computational physics; Algorithm; Nuclear magnetic resonance; Computer science; Physics; Condensed matter physics; Optics","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":[0.0003393304,0.0001198701,0.0001822327,0.0001335741,0.0001138998,0.00001541393,0.0002797064,0.00004052387,0.00002610385],"category_scores_gemma":[0.00005558463,0.0001216492,0.000132744,0.0001921464,0.00003732178,0.000005116518,0.0001981531,0.0002063484,9.846101e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007941369,"about_ca_system_score_gemma":0.000114543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001168516,"about_ca_topic_score_gemma":0.000006044738,"domain_scores_codex":[0.9988467,0.0000943753,0.0003602144,0.0001749841,0.0002726024,0.0002510885],"domain_scores_gemma":[0.9994757,0.00000579165,0.0002329241,0.0001676874,0.00006800247,0.00004995495],"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":[0.00005892102,0.00009121962,0.002058737,0.000004422416,0.00006007764,0.0001047497,0.0001568913,0.0005990026,0.9958111,0.00004206898,0.0002206438,0.0007921327],"study_design_scores_gemma":[0.002303765,0.002245222,0.002705524,0.00004907948,0.00003121503,0.0005928547,0.001417237,0.00004761061,0.9479737,0.0002701122,0.04190916,0.0004545283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701194,0.02335656,0.005473007,0.0002107192,0.0001934536,0.00009201171,0.000004027977,0.000003242724,0.000547528],"genre_scores_gemma":[0.9910591,0.00033832,0.008132503,0.0002683185,0.0001114921,0.00001221055,0.000006234947,0.00002082787,0.00005105112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04783744,"threshold_uncertainty_score":0.496071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977549181943543,"score_gpt":0.3065758520505899,"score_spread":0.2868003602311545,"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."}}