Carbon and Greenhouse gas dynamics at an industrial cutaway peatland
Bibliographic record
Abstract
Worldwide, peatlands are estimated to store around 30% of soil organic carbon on only 3% of the land area. In Ireland, these numbers increase with peatlands covering ~20% of the land area and storing up to 75% of the terrestrial soil organic carbon. However, a large proportion (≥90%) of these ecosystems have been degraded through drainage for agriculture, forestry, horticulture and extraction for energy. With the increase in global initiatives for the conservation, rehabilitation and sustainable management of peatland, further investigation of the effect of drainage and rehabilitation is needed to better understand the carbon and greenhouse gas (GHG) dynamics of these ecosystems. Additionally, it is crucial to understand the natural adaptive capacity of the ecosystem to further inform effective rehabilitation strategies. This study investigated the carbon dioxide (CO2) and methane (CH4) fluxes from a former industrial peat extraction site in Ireland, prior to rehabilitation using static chamber techniques. The site is an overall source of CO2, releasing a cumulative annual flux of 9 g C-CO2 m-2 y-1 for 2020-2021 and a small source of methane, releasing an average annual cumulative total of 1 g C-CH4 m-2 y-1. This research highlights the potential emissions savings that can be made through rehabilitation as water tables increase with rewetting and these sites become re-vegetated. However, long-term measurements to track the temporal dynamics of C/GHG emissions post-rehabilitation are required to fully assess the climate mitigation of this approach, particularly in light of a changing climate which might further influence the ecological, hydrological, and biogeochemical functions of these important ecosystems.
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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 | 0.001 | 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".