Nonlinear Response of Riverine N<sub>2</sub>O Fluxes to Oxygen and Temperature
Bibliographic record
Abstract
One-quarter of anthropogenically produced nitrous oxide (N2O) comes from rivers and estuaries. Countries reporting N2O fluxes from aquatic surfaces under the United Nations Framework Convention on Climate Change typically estimate anthropogenic inorganic nitrogen loading and assume a fraction becomes N2O. However, several studies have not confirmed a linear relationship between dissolved nitrate (NO3-) and river N2O fluxes. We apply recursive partitioning analysis to examine the relationships between N2O flux and NO3-, dissolved oxygen (DO), temperature, land use and surficial geology in the Grand River, Canada, a seventh-order river in an agricultural catchment with substantial urban population. Results suggest that N2O flux is high when hypoxia exists. Temperature, not NO3-, was the primary correlate of N2O flux when hypoxia does not occur suggesting NO3- is not limiting N2O production and further increases in NO3- may not lead to comparable increases in N2O flux. This work indicates that a linear relationship between NO3- and N2O is unlikely to exist in most agricultural and urban impacted river systems. Most N2O is produced during hypoxia so quantifying the extent of hypoxia is a necessary first step to quantifying N2O fluxes in lotic systems. Predicted increases in riverine hypoxia via eutrophication and increased temperature due to climate change may drive nonlinear increases in N2O production.
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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".