Temporal variability in dissolved organic matter composition export in streams
Bibliographic record
Abstract
Export of dissolved organic carbon (DOC) from freshwater systems has been the focus of many studies owing to its pivotal role in regulating global carbon fluxes and ecosystem function. Both the flux and composition of dissolved organic matter (DOM) are critical for understanding its ecological impact, as similar compositions can have vastly different consequences depending on the magnitude of input and hydrological context. However, very little data exists on the composition of DOM export fluxes to downstream ecosystems. Here we investigate the interaction of water temperature and discharge on DOC and DOM composition export fluxes in two streams draining contrasting watersheds (agriculture versus forested) in southern Ontario, Canada across seasons. Using Generalized Additive Models, we observed that both stream discharge and water temperature significantly affected DOM composition, and the proportion of terrestrial humic-like DOM exhibited strong positive relationship with discharge. Although DOC loads were comparable between the two streams, the export loads and fluxes of DOM composition (in terms of fluorescent loads and fluxes) differed significantly. These patterns of DOM composition fluxes in both streams remained consistent across seasons, suggesting that watershed characteristics and nutrient availability primarily govern DOM dynamics and export, while seasonal drivers such as discharge and temperature further modulate these patterns. Export loads and fluxes of DOM components were higher in spring and winter months compared to summer and autumn in both streams, while fluxes also increased at medium (Q10-Q90) and high flow (> Q10) at a variable extent in the contrasting streams. Temperature and discharge regulated export of DOM can be further affected with changing climate and increasing frequency of extreme events and alter the processing and delivery of DOM to downstream ecosystems. Supplementary Information: The online version contains supplementary material available at 10.1007/s10533-025-01270-3.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".