Assessing nonpoint‐source nitrogen loading and nitrogen fixation in lakes using <i>δ</i><sup>15</sup>N and nutrient stoichiometry
Bibliographic record
Abstract
Runoff from human‐dominated watersheds has greatly altered nitrogen (N) and phosphorus (P) cycling in lakes. Nutrients from human sources are distinct from those from undisturbed ecosystems in several ways including lower N : P ratios, which can drive ecosystems to N‐limited conditions, and enriched stable N isotope ratios. In this study, we used these distinct characteristics to estimate shifts in N sources to 27 lakes across a human density gradient in western Washington. We compared an N stable isotope two‐source mixing model with a mixing model that coupled N stable isotopes to N : P stoichiometry and included N fixation. We found that a two‐source mixing model (human and watershed sources) did not explain observed variation in δ15N of particulate organic matter (POM) and primary consumers (R2 = 0.60) as well as a model that included a third N source (N fixation; R2 = 0.72). When fixed N was facultatively added to the ecosystem below a critical N : P ratio, the more complex mixing model captured the observed patterns in POM and primary‐consumer δ15N among lakes extremely well. In lakes with P concentrations > 20 µg L−1 (N : P mass ratio < 15.3), N fixation became an increasingly important component of the N cycle, accounting for > 50% of lake N budgets. This model provides a novel way to estimate the contribution of nonpoint N sources and N fixation to lakes in watersheds subject to human nutrient inputs.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".