Differential Controls of Greenhouse Gas (CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O) Concentrations in Natural and Constructed Agricultural Waterbodies on the Northern Great Plains
Bibliographic record
Abstract
Abstract Inland waters are hotspots of greenhouse gas (GHG) cycling, with small water bodies particularly active in the production and consumption of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). However, wetland ponds are being replaced rapidly by small constructed reservoirs in agricultural regions, yet it is unclear whether these two water body types exhibit similar physical, chemical, and environmental controls of GHG content and fluxes. Here, we compared the content and regulatory mechanisms of all three major GHGs in 20 pairs of natural wetland ponds and constructed reservoirs in Canada's largest agricultural region. Carbon dioxide content was associated primarily with metabolic indicators in both water body types; however, primary production was paramount in reservoirs, and heterotrophic metabolism a stronger correlate in wetland ponds. Methane concentrations were correlated positively with eutrophication of the reservoirs alone, while competitive inhibition by sulfur‐reducing bacteria may have limited CH4 in both waterbody types. Contrary to expectations, N2O was undersaturated in both water body types, with wetlands being a significantly stronger and more widespread N2O sink. Varying regulatory processes are attributed to differences in age, depth, morphology, and water‐column circulation between water body types. These results suggest that natural and constructed water bodies should be modeled separately in regional GHG budgets.
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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 | 0.001 | 0.001 |
| 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".