Widespread nitrous oxide undersaturation in farm waterbodies creates an unexpected greenhouse gas sink
Bibliographic record
Abstract
Nitrogen pollution and global eutrophication are predicted to increase nitrous oxide (N 2 O) emissions from freshwater ecosystems. Surface waters within agricultural landscapes experience the full impact of these pressures and can contribute substantially to total landscape N 2 O emissions. However, N 2 O measurements to date have focused on flowing waters. Small artificial waterbodies remain greatly understudied in the context of agricultural N 2 O emissions. This study provides a regional analysis of N 2 O measurements in small (<0.01 km 2 ) artificial reservoirs, of which an estimated 16 million exist globally. We show that 67% of reservoirs were N 2 O sinks (−12 to −2 μmol N 2 O⋅m −2 ⋅d −1 ) in Canada’s largest agricultural area, despite their highly eutrophic status [99 ± 289 µg⋅L −1 chlorophyll-a (Chl- a )]. Generalized additive models indicated that in situ N 2 O concentrations were strongly and nonlinearly related to stratification strength and dissolved inorganic nitrogen content, with the lowest N 2 O levels under conditions of strong water column stability and high algal biomass. Predicted fluxes from previously published models based on lakes, reservoirs, and agricultural waters overestimated measured fluxes on average by 7- to 33-fold, challenging the widely held view that eutrophic N-enriched waters are sources of N 2 O.
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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".