Measurement and modeling of surface-atmosphere exchange of carbon dioxide and methane in a cattail marsh in eastern Ontario
Bibliographic record
Abstract
Wetlands exchange significant amounts of carbon dioxide (CO2) and methane (CH4)---two major greenhouse gases (GHG), and thus have significant impacts on the Earth's climate. In this study, fluxes of CO2 and CH4 were measured in a cattail-dominated marsh in Eastern Ontario, Canada. Eddy covariance measurements of net ecosystem CO2 exchange (NEE) revealed that the marsh was an annual sink of 264 g C m-2, and that growing season net CO2 fluxes were strongly correlated with vegetation biomass and leaf area index. Fluxes of CH4 were measured from water, soil and plants using closed chambers and resulted in a net annual area-weighted emission from the marsh of 206 g C m-2. Consequently, the net (CO2 + CH4) annual carbon (C) balance of this wetland corresponded to a sink of 58 g C m-2. A simple radiative forcing model based on the marsh CO 2 and CH4 emission patterns suggests that, despite the net carbon uptake, this wetland is contributing to atmospheric warming because of the large CH4 efflux. Future potential climate impacts of this marsh were evaluated using different emission scenarios that could result in response to climatic or environmental changes. Overall, short-term impacts are driven by CH4 emission rate, while the CO2 flux determines the impacts on longer time horizons. Uncertainties in predicting future wetland GHG balance arise from uncertain feedbacks and responses. Future alterations of the marsh GHG emission and uptake patterns resulting from land use or climatic changes could lead to a shift in the marsh C balance, showing the importance of wetland ecosystems in national and global C budgets and GHG-related political decisions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".