Contrasting Seasons and Land Uses Alter Riverine Dissolved Organic Matter Composition
Bibliographic record
Abstract
Abstract Different sources and fates control riverine dissolved organic matter (DOM) composition in catchments of contrasting land use, and climate. However, assessing the changes in DOM composition together with nutrient forms along rivers exposed to these gradients remains rare. Here we quantified the spatial and temporal patterns in DOM components and nutrient forms along the mainstem of a 5th order river through sequential forested, urban, and agricultural reaches during low flow moments in summer and winter, and two contrasting springs, one with a historically rare flooding event. There were widespread abrupt shifts in the composition of DOM in low flow seasons that coincided with changes in land use whose sources could be inferred by endmember samples and nutrient changes. Compared to summer, DOM pools considered bio- (microbial-like) and photo-labile (Peak C) tended to dominate during the winter, along with reactive nutrient forms (ammonium, dissolved phosphorus). This implied higher processing potentials during summer, where microbial-humic-like DOM and nitrate dominated, and accumulation of reactive forms during winter. DOM composition remained relatively stable under typical flow conditions with reduced retention time and processing, but major shifts were observed during an extreme flood year, pointing to unusual loadings of highly labile sources of DOM. Overall we found that, despite relatively small changes in the quantity of DOM flowing in this north temperate river, there were major spatial and temporal shifts in its composition and associated nutrients that reflected contrasting loading and processing potentials depending on land use and seasonal patterns in temperature and hydrology.
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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 | 0.000 |
| 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) | 0.001 | 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".