Direct 1H NMR spectroscopy of dissolved organic matter in natural waters
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy arguably provides the greatest insight into the overall chemical composition of dissolved organic matter (DOM). However, in a standard 5 mm NMR probe, a sample of sea water at natural abundance only contains ca. 500-600 ng of organic matter, distributed among the heterogeneous components of DOM. Additionally, the intensity of the water signal, which may be many orders of magnitude greater than the signals from DOM, makes the detection and analysis of DOM at natural abundance extremely demanding. Here, we demonstrate, that although challenging, the application of an improved water suppression technique allows NMR spectra of DOM to be obtained directly (i.e without pre-concentration) for major bodies of water, including rivers, lakes and the ocean. The technique described here provides a compositional overview of an intact sample, permitting researchers to investigate and assess the impact of concentration, isolation and extraction procedures that are employed routinely. Also the technique permits NMR to be performed on 'precious' samples for which traditional isolations are not possible, for example, water from ice cores and pore water, which are key in hydrology and for paleoclimatic reconstruction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".