Impacts of Active Versus Passive Re‐Wetting on the Carbon Balance of a Previously Drained Bog
Bibliographic record
Abstract
Abstract Peatland drainage depletes large carbon stocks by increasing carbon dioxide (CO2) emissions from the soil. Restoration via re‐wetting could play an important role in climate change mitigation, reducing CO2 emissions and increasing C storage within peatlands. However, re‐wetting leads to a biogeochemical compromise between increased CO2 uptake, and enhanced methane (CH4) release. The extent of this compromise in re‐wetted ecosystems with differing environmental conditions is uncertain. To assess re‐wetting effects, we analyzed eddy‐covariance flux measurements from a temperate bog near Vancouver, Canada, from two sites that have undergone different restoration techniques. By the end of the 1‐year study period, the actively re‐wetted, wetter site, was a weak CO2 sink (−26.1 ± 6.1 g C‐CO2 m−2), and the passively re‐wetted, drier site, was near CO2 neutral (3.8 ± 3.1 g C‐CO2 m−2). Higher CH4 emissions at the wetter site led to a larger radiative balance on 20‐ and 100‐year time horizons, implying that the strong radiative effect of CH4 can offset CO2 sink strength on shorter to medium timeframes. However, long‐term radiative forcing (RF) modeling suggests sustained CO2 uptake by the wetter site will eventually lead to a cooling effect on the climate. Furthermore, modeling results emphasize that despite both re‐wetted peatland sites having a positive RF over century timescales, the lack of restoration would have resulted in a significantly larger RF beyond the first few decades following restoration. Results highlight the importance of actively re‐wetting disturbed peatlands to mitigate climate warming and can be used to inform management 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".