Experimental Ecosystem Eutrophication Causes Offsetting Effects on Emissions of CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O from Agricultural Reservoirs
Bibliographic record
Abstract
Despite decades of research and management efforts, eutrophication remains a persistent threat to inland waters. As nutrient pollution intensifies in the coming decades, the implications for aquatic greenhouse gas (GHG) emissions are poorly defined, particularly the responses of individual GHGs: carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O). The biogeochemical controls of each gas can differ, making it difficult to predict the overall effect of nutrient pollution on the net radiative forcing of aquatic ecosystems. Here, we induced eutrophication of small nitrogen (N)-limited agricultural reservoirs and measured changes in diffusive GHG emissions within a before-after-control-impact (BACI) study design during June to September 2021. Each gas exhibited a unique response to 300% increases in primary production, with a shift from an overall CO 2 source to a sink, a modest increase in N 2 O flux, and, unexpectedly, no significant change in CH 4 emissions. The lack of net directional change in CO 2 -equivalent GHG emissions in fertilized reservoirs during the summer contrasts findings from empirical studies of eutrophic lakes. Our findings illustrate the difficulty in extrapolating among different sized ecosystems and suggest that forecast 2-fold increases in agricultural N fertilization by 2050 may not result in consistently elevated GHG emissions during summer, at least from small reservoirs in continental grassland regions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".