High-Resolution Four-Dimensional <sup>1</sup>H−<sup>13</sup>C NOE Spectroscopy using Methyl-TROSY, Sparse Data Acquisition, and Multidimensional Decomposition
Bibliographic record
Abstract
An approach for recording four-dimensional (4D) methyl (1)H-(13)C-(13)C-(1)H NOESY spectra with high resolution and sensitivity is presented and applied to Malate Synthase G (723 residues, 82 kDa). Sensitivity and resolution have been optimized using a highly deuterated, methyl-protonated sample in concert with methyl-TROSY, sparse data sampling in the three indirect dimensions, and 4D spectral reconstruction using multidimensional decomposition (MDD). A sparse data acquisition protocol is introduced that ensures that sufficiently long indirect acquisition times can be employed to exploit the decreased relaxation rates associated with methyl-TROSY, without increasing the duration of the 4D experiment beyond acceptable measurement times. In this manner, only a fraction ( approximately 30%) of the experimental data that would normally be needed to achieve a spectrum of high resolution is acquired. The reconstructed 4D spectrum is of similar resolution and sensitivity to three-dimensional (3D) (13)C-edited NOE spectra, is straightforward to analyze, and resolves ambiguities that emerge when 3D data sets only are considered.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".