Different forms of carbon, nitrogen, and phosphorus influence ecosystem stoichiometry in a north temperate river across seasons and land uses
Bibliographic record
Abstract
Abstract Natural and human features on land result in differential loadings of carbon (C), nitrogen (N), and phosphorus (P) to rivers that influence within ecosystem processing. However, little is known about how land use, together with seasonal changes in climate and hydrology, influence the relative proportions of C, N, and P in rivers. To evaluate the spatial and temporal patterns in ecosystem‐level C: N: P stoichiometry, we sampled 13 sites once per season for 3 yr along the main stem of a north temperate river with winter ice‐cover that flows across a gradient of forested, urban, and agricultural landscapes. We found that C concentrations were rather stable along the continuum, whereas N and P rapidly increased downstream due to urban and agricultural land uses. The flow‐weighted C: N: P ecosystem stoichiometry ranged from 2319: 119: 1 in the most upstream site to 368: 60: 1 at the outlet. The dominant form of N generally shifted from dissolved organic nitrogen in upstream forested reaches to nitrate in more impacted, downstream reaches, and winter stoichiometry was enriched in inorganic N and dissolved P forms. Concentrations of all three elements were generally lower in spring during year‐high flow due to dilution. The spatial and temporal variation in stoichiometry in this north temperate river covered much of the range previously observed between litter ratios and the Redfield ratio. This suggests that even moderate human impacts can have profound effects on riverine ecosystem stoichiometry, and that these effects are modulated by seasonal trends 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) | 0.000 | 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 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".