Rapid estimation of nuclear magnetic resonance experiment time in low-concentration environmental samples
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is an essential tool for studying environmental samples but is often hindered by low sensitivity, especially for the direct detection of nuclei such as(13) C. In very heterogeneous samples with NMR nuclei at low abundance, such as soils, sediments, and air particulates, it can take days to acquire a conventional(13) C spectrum. The present study describes a prescreening method that permits the rapid prediction of experimental run time in natural samples. The approach focuses the NMR chemical shift dispersion into a single spike, and, even in samples with extremely low carbon content, the spike can be observed in two to three minutes, or less. The intensity of the spike is directly proportional to the total concentration of nuclei of interest in the sample. Consequently, the spike intensity can be used as a powerful prescreening method that answers two key questions: (1) Will this sample produce a conventional NMR spectrum? (2) How much instrument time is required to record a spectrum with a specific signal-to-noise (S/N) ratio? The approach identifies samples to avoid (or pretreat) and permits additional NMR experiments to be performed on samples producing high-quality NMR data. Applications in solid- and liquid-state(13) C NMR are demonstrated, and it is shown that the technique is applicable to a range of nuclei.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".