Eddy covariance data reveal that a small freshwater reservoir emits a substantial amount of carbon dioxide and methane
Bibliographic record
Abstract
Small freshwater reservoirs are ubiquitous and likely play an important role in global greenhouse gas (GHG) budgets relative to their limited water surface area. However, constraining annual GHG fluxes in small freshwater reservoirs is challenging given their footprint area and spatially and temporally variable emissions. To quantify the GHG budget of a small reservoir, we deployed an eddy covariance system in a small (0.1 km 2 ) reservoir located in southwestern Virginia, USA for a full year to measure carbon dioxide (CO 2 ) and methane (CH 4 ) fluxes near-continuously. Fluxes were coupled with in situ sensors measuring multiple environmental parameters. Throughout the year, we found the reservoir to be a substantial source of CO 2 (~600 g CO 2 -C m -2 yr -1 ) and CH 4 (~1.0 g CH 4 -C m -2 yr -1 ) to the atmosphere, with significant sub-daily, daily, weekly, and approximately monthly timescales of variability. Importantly, we found annual GHG emissions estimated using eddy covariance were over an order of magnitude greater than diffusive GHG fluxes measured weekly to biweekly. During the winter, we found GHG fluxes during partial ice-on and open-water conditions were not statistically different, suggesting reservoirs may play an important role in freshwater GHG budgets throughout the year, not just during the open-water period. Finally, we identified several key environmental variables that may be driving GHG fluxes, specifically, surface water temperature and dissolved oxygen concentrations. Overall, our novel year-round eddy covariance data from a small reservoir indicate that these freshwater ecosystems likely contribute a substantial amount of CO 2 and CH 4 to global 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 | 0.001 |
| 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".