Seasonal patterns and key chemical predictors of dissolved greenhouse gases in small prairie pothole ponds
Bibliographic record
Abstract
Shallow ponds can provide ideal conditions for production of greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), and thus are important to include in global and regional GHG budgets. The Canadian Prairie Pothole Region contains millions of shallow natural ponds, and we investigated GHG dynamics in 145 ponds across the region. Ponds were consistently supersaturated with CH4, often supersaturated with CO2 (57% occurrence), and often undersaturated with N2O (65% occurrence). Spring measurements showed higher N2O saturation ( p = 0.0037) than summer, while summer had higher CH4 ( p < 0.001) and CO2 ( p = 0.023) saturation than spring. Ponds exhibited large physicochemical variation, yet sulfate concentration and pH were strong predictors of dissolved CH4 and CO2, respectively. No predictor was identified for N2O. The link between sulfate and CH4 has important implications as dissolved CH4 in low sulfate (<178 mg L−1) systems was much more responsive to changes in temperature. This research fills an important knowledge gap about the GHG dynamics of prairie pothole ponds and the role of water chemistry for diffuse GHG release. Our work can also be used in ongoing efforts to describe ecosystem services (or disservices) assigned to ponds in this agriculture-dominated region.
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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 | 0.001 |
| 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) | 0.001 | 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".