The application of LC-NMR and LC-SPE-NMR to compositional studies of natural organic matter
Bibliographic record
Abstract
Non-living natural organic matter (NOM) is ubiquitous in the oceans, atmosphere, sediments, and soils, and represents the most abundant organic carbon reserves on earth. However, a large proportion is considered to be "molecularly uncharacterized" because the inherent complexity of NOM is problematic when applying conventional analytical techniques. This manuscript presents initial applications of LC-NMR (1H) and LC-SPE-NMR (1H) to the studies of NOM isolated from water and soil. LC-NMR is applied to dissolved natural organic matter (DNOM) collected from freshwater environments, and both LC-NMR and LC-SPE-NMR are applied to an alkaline soil extract. The polar and complex nature of the DNOM samples limits conventional reversed phase separation, which can be partially overcome with the use of an ion pair reagent, although such an approach further complicates the NMR detection. LC-SPE-NMR of the soil alkaline extract was encouraging, and specific components in the mixture could be assigned. This work demonstrates that it is both possible to separate and concentrate specific components in NOM such that NMR detection is possible. As NMR information will be critical in unraveling the novel and/or complex structures in NOM this represents a key analytical hurdle in this area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".