HILIC-NMR: Toward the Identification of Individual Molecular Components in Dissolved Organic Matter
Bibliographic record
Abstract
This article presents research targeted toward the isolation and detection of unique molecular structures from what is believed to be the world's most complex organic mixture: dissolved organic matter (DOM). Hydrophilic interaction chromatography (HILIC) was used to separate Suwannee River DOM (SRDOM) into 80 fractions, simplified to the extent that detection with nuclear magnetic resonance spectroscopy (NMR) results in many sharp signals that are indicative of individual compounds, some of which are identifiable with multidimensional NMR. Parallel factor analysis (PARAFAC) of fluorescence excitation-emission matrices (EEMs) was additionally employed on HILIC-simplified fractions to further confirm the effectiveness of the HILIC separations as well as draw insight into how structural characteristics relate to DOM fluorescence signals. Findings suggest that material believed to be derived from both cyclic and linear terpenoids was dominant in the most hydrophobic fractions as were the majority of the fluorescence signals, whereas hydrophilic material was highly correlated with carbohydrate-type structures as well as high contributions from amino acid fluorescence. NMR spectra of DOM, typically featureless mounds, are substantially more detailed with HILIC-simplified fractions to the point where hundreds of signals are present and 2D NMR correlations permit significant structural identifications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".