Advances in NMR spectroscopy of small molecules in solution
Bibliographic record
Abstract
This chapter discusses advances in small molecule NMR in solution and covers articles from 2020 or 2021. After a short introduction, the second, major, section covers the determination of three-dimensional structures of organic molecules in solution. New methods for acquiring and interpreting residual dipolar coupling and residual chemical shift anisotropies are discussed, along with new orienting media to measure these parameters. The use of density functional theory calculations to aid in 3D structure elucidation is covered. Several new machine learning and artificial intelligence programs that aid in identifying unknown compounds are described. The third section covers pulse sequence developments and means of more rapid data acquisition. Using sequential and/or simultaneous acquisition of two to ten spectra in a single experiment is featured. Discussion of non-uniform sampling mainly focuses on choices for acquiring and processing NUS data sets. The fourth section covers developments in benchtop NMR spectrometers, including hardware improvements, methods for overcoming their limited sensitivity and real-life applications of these spectrometers. The final section covers the use of NMR for investigating complex mixtures, including programs designed to identify individual components in natural product mixtures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".