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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".