What’s in an EEM? Molecular Signatures Associated with Dissolved Organic Fluorescence in Boreal Canada
Bibliographic record
Abstract
Dissolved organic matter (DOM) is a master variable in aquatic systems. Modern fluorescence techniques couple measurements of excitation emission matrix (EEM) spectra and parallel factor analysis (PARAFAC) to determine fluorescent DOM (FDOM) components and DOM quality. However, the molecular signatures associated with PARAFAC components are poorly defined. In the current study we characterized river water samples from boreal Québec, Canada, using EEM/PARAFAC analysis and ultrahigh resolution mass spectrometry (FTICR-MS). Spearman's correlation of FTICR-MS peak and PARAFAC component relative intensities determined the molecular families associated with 6 PARAFAC components. Molecular families associated with PARAFAC components numbered from 39 to 572 FTICR-MS derived elemental formulas. Detailed molecular properties for each of the classical humic- and protein-like FDOM components are presented. FTICR-MS formulas assigned to PARAFAC components represented 39% of the total number of formulas identified and 59% of total FTICR-MS peak intensities, and included significant numbers compounds that are highly unlikely to fluoresce. Thus, fluorescence measurements offer insight into the biogeochemical cycling of a large proportion of the DOM pool, including a broad suite of unseen molecules that apparently follow the same gradients as FDOM in the environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".