Seasonal variability of CO2, CH4, and N2O content and fluxes in small agricultural reservoirs of the northern Great Plains
Bibliographic record
Abstract
Inland waters are important global sources, and occasional sinks, of CO2, CH4, and N2O to the atmosphere, but relatively little is known about the contribution of GHGs of constructed waterbodies, particularly small sites in agricultural regions that receive large amounts of nutrients (carbon, nitrogen, phosphorus). Here, we quantify the magnitude and controls of diffusive CO2, CH4, and N2O fluxes from 20 agricultural reservoirs on seasonal and diel timescales. All gases exhibited consistent seasonal trends, with CO2 concentrations highest in spring and fall and lowest in mid-summer, CH4 highest in mid-summer, and N2O elevated in spring following ice-off. No discernible diel trends were observed for GHG content. Analyses of GHG covariance with potential regulatory factors were conducted using generalized additive models (GAMs) that revealed CO2 concentrations were affected primarily by factors related to benthic respiration, including dissolved oxygen (DO), dissolved inorganic nitrogen (DIN), dissolved organic carbon (DOC), stratification strength, and water source (as δ18Owater). In contrast, variation in CH4 content was correlated positively with factors that favoured methanogenesis, and so varied inversely with DO, soluble reactive phosphorus (SRP), and conductivity (a proxy for sulfate content), and positively with DIN, DOC, and temperature. Finally, N2O concentrations were driven mainly by variation in reservoir mixing (as buoyancy frequency), and were correlated positively with DO, SRP, and DIN levels and negatively with pH and stratification strength. Estimates of mean CO2-eq flux during the open-water period ranged from 5,520 mmol m−2 year1 (using GAM-predictions) to 10,445 mmol m−2 year−1 (using interpolations of seasonal data) reflecting how extreme values were extrapolated, with true annual flux rates likely falling between these two estimates.
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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 | 0.001 | 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".