Global impacts of sulfate deposition from acid rain on methane emissions from natural wetlands
Bibliographic record
Abstract
Natural wetlands form the largest methane (CH4) source to the atmosphere. A collection of recent field and laboratory studies point to an anthropogenic control on CH4 emissions from these systems: acid rain sulfate (SO42-) deposition. These studies ranging from the UK, USA, Canada, Sweden and Czech Republic demonstrate that low rates of SO42- deposition, within the range commonly experienced in acid rain impacted regions, can suppress CH4 emissions by as much as 40% and that the response of CH4 emissions to increasing rates of SO42- deposition closely mirrors changes in sulfate reduction rates with SO42- deposition. This indicates that the suppression in CH4 flux is the result of acid rain stimulating a competitive exclusion of methanogenesis by sulfate reducing bacteria, resulting in reduced methane production. These findings were extrapolated to the global scale by combining modelled, spatially explicit data sets of CH4 emission from wetlands across the globe with modelled S deposition. Results indicate that this interaction may be important at the global scale, suppressing CH4 emissions from wetlands in 2030 by as much as 20--28Tg, and, in the process, offsetting predicted climate induced growth in the wetland CH4 source.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".