{"id":"W2988834127","doi":"10.1002/pro.3785","title":"Temperature dependence of NMR chemical shifts: Tracking and statistical analysis","year":2019,"lang":"en","type":"article","venue":"Protein Science","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Chemical shift; Chemistry; Isotropy; Proton; Amide; Curvature; Nuclear magnetic resonance spectroscopy; Atmospheric temperature range; Linearity; Nuclear magnetic resonance; Thermodynamics; Physical chemistry; Physics; Stereochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005959066,0.0007617885,0.0007738855,0.003128523,0.0004823401,0.0008997575,0.001168904,0.0007637233,0.001330277],"category_scores_gemma":[0.02058728,0.0003959142,0.001113782,0.00208171,0.0006391305,0.001099885,0.0007521176,0.001364693,0.000689021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007693531,"about_ca_system_score_gemma":0.001484425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005159638,"about_ca_topic_score_gemma":0.002998765,"domain_scores_codex":[0.9981248,0.0007206166,0.0001160754,0.0005124797,0.0004494877,0.00007643353],"domain_scores_gemma":[0.9878179,0.008962878,0.0009811821,0.001108945,0.0009968241,0.0001323567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006006204,0.0004586321,0.04987818,0.0002944677,0.0006198597,0.0002577131,0.0004484148,0.5555756,0.02670462,0.02782823,0.006222162,0.3311116],"study_design_scores_gemma":[0.000005968182,0.00002934778,0.00261148,0.000006406999,0.00001669463,0.00002749884,0.0000100343,0.9907712,0.00340791,0.002461079,0.0006293055,0.00002305245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08205902,0.0001260144,0.9123347,0.0001107384,0.00002952981,0.0001476589,0.0007420846,0.003869031,0.000581152],"genre_scores_gemma":[0.4085758,0.0001487134,0.5867048,0.0000588178,0.0000394944,0.0005869622,0.002163885,0.0007378423,0.0009837667],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005959066,"threshold_uncertainty_score":0.03151494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004396683031408381,"score_gpt":0.2479278037685122,"score_spread":0.2435311207371038,"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."}}