Hydrology and Trophic Status Control Lake Dissolved Organic Matter Concentration and Composition at a Continental Scale
Bibliographic record
Abstract
Abstract Dissolved organic matter (DOM) is a key component of the lake biogeochemistry. Hydrology link variables influencing lake DOM at local and watershed scales, but its role at macroscales remains less understood. We studied the DOM concentration and composition from 548 lakes across the five major Canadian continental basins using absorption spectroscopy and parallel factor analysis, and ultra‐high resolution mass spectroscopy, and linked this to deuterium excess (d‐excess), derived from stable water isotopes as a proxy for evaporation, water residence time, and regional hydrology. DOM concentration and composition varied greatly within and across basins, with strong correlations between molecular and optical properties. At a continental scale, d‐excess and TP concentration were the main drivers of DOM concentration and composition. TP positively influenced DOM concentration, and specific DOM components (e.g., Aliphatics), suggesting nutrient‐driven effects on lake metabolism that varied regionally. DOM concentration declined with d‐excess, but the relationships between individual DOM molecular composition classes and d‐excess differed among components and basins, resulting in regional differences in DOM composition along hydrologic gradients. The inferred source composition DOM based on these patterns had subtle regional differences, with Aliphatics related to the average regional altitude and Aromatics related to the average regional soil organic content. We show that DOM processing along the hydrologic continuum is the key factor establishing differences in DOM composition in lakes at a continental scale. Overall, TP influenced DOM through effects on primary production and metabolism, whereas d‐excess integrated the selective degradation and accumulation of DOM along the aquatic network.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".