Effects of Damming on River Nitrogen Fluxes: A Global Analysis
Bibliographic record
Abstract
Abstract Damming creates biogeochemical hotspots along rivers that modify the riverine flow of nutrients, including nitrogen (N). Here, we quantify the impact of dams on global riverine N fluxes using a reservoir N mass balance model. In‐reservoir processes represented in the model include primary production, mineralization of organic N, denitrification, and sedimentary burial. In addition, we explicitly account for N fixation as a source of N, assuming that the N to phosphorus (P) ratio of the inflow regulates the magnitude of N fixation in reservoirs. The model is scaled up via a Monte Carlo analysis that yields global relationships between N elimination in reservoirs, either by denitrification or burial, and the hydraulic residence time. These relationships are then combined with N loads to the world's dam reservoirs generated by the Global‐NEWS model and the estimated N fixation fluxes. According to the results, in year 2000, worldwide N fixation in reservoirs was on the order of 70 Gmol yr −1 , while denitrification and burial in reservoirs eliminated around 270 Gmol yr −1 , equal to 7% of N loading to the global river network. The latter is predicted to double to 14% by 2030, mainly as a result of the current boom in dam building. The results further imply that, largely due to N fixation in reservoirs, damming causes a global upward shift in riverine N:P ratios, thus lessening N limitation in receiving water bodies.
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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 | 0.001 |
| 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 | 0.001 |
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".