{"id":"W3210504932","doi":"10.1021/acs.jpcb.1c07397","title":"Racing toward Fast and Effective <sup>17</sup>O Isotopic Labeling and Nuclear Magnetic Resonance Spectroscopy of N-Formyl-MLF-OH and Associated Building Blocks","year":2021,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bruker (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Mitacs; Canada Research Chairs; Canada Foundation for Innovation; Simons Foundation","keywords":"Biomolecule; Nuclear magnetic resonance spectroscopy; Magic angle spinning; Spectroscopy; Chemistry; Nuclear magnetic resonance; Solid-state nuclear magnetic resonance; Population; Polarization (electrochemistry); Molecule; Analytical Chemistry (journal); Materials science; Physics; Nanotechnology; Physical chemistry; Organic chemistry","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":[0.0004541239,0.0003969767,0.0001831146,0.0001893944,0.0002597237,0.0003107459,0.0005497815,0.0004083478,0.001640135],"category_scores_gemma":[0.0004528407,0.0001426999,0.0001846984,0.0001739973,0.0004005991,0.0006588095,0.0003888934,0.0007107946,0.0008810418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002459799,"about_ca_system_score_gemma":0.0003080296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000570524,"about_ca_topic_score_gemma":0.001008425,"domain_scores_codex":[0.9997725,0.00003413239,0.000009446602,0.00005547149,0.00008702025,0.00004144101],"domain_scores_gemma":[0.9997143,0.00006955408,0.00008665123,0.00004867202,0.00005131341,0.00002944695],"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":[0.0001047626,0.00003188052,0.0002320694,0.0000943752,0.000006138444,0.0001183228,0.00005591625,0.0005640485,0.9774848,0.00282033,0.0006588416,0.01782849],"study_design_scores_gemma":[0.000006612982,0.0001078621,0.0001839384,0.000004957034,0.000003143172,0.0001280534,0.00001116844,0.001272054,0.9872999,0.0002249589,0.01074941,0.000007985326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7644587,0.004794044,0.2092665,0.001121772,0.0002386826,0.0002669676,0.0007029385,0.001301307,0.01784913],"genre_scores_gemma":[0.7989333,0.003248829,0.1840095,0.0005428988,0.00008306846,0.000300629,0.0009171216,0.0003177226,0.0116469],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001640135,"threshold_uncertainty_score":0.005486846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005554761957445643,"score_gpt":0.2529078839207598,"score_spread":0.2473531219633142,"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."}}