Seasonal variability of CO2, CH4, and N2O content and fluxes in small agricultural reservoirs of the northern Great Plains
Why this work is in the frame
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Bibliographic record
Abstract
Inland waters are important global sources, and occasional sinks, of CO 2 , CH 4 , and N 2 O 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 CO 2 , CH 4 , and N 2 O fluxes from 20 agricultural reservoirs on seasonal and diel timescales. All gases exhibited consistent seasonal trends, with CO 2 concentrations highest in spring and fall and lowest in mid-summer, CH 4 highest in mid-summer, and N 2 O 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 CO 2 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 δ 18 O water ). In contrast, variation in CH 4 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, N 2 O 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 CO 2 -eq flux during the open-water period ranged from 5,520 mmol m −2 year 1 (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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 it