Above and belowground carbon stocks among organic soil wetland types, accounting for peat bathymetry
Bibliographic record
Abstract
Wetlands are widely recognized for their carbon (C) sequestration capacity and importance at mitigating climate change. Yet, to best inform regional conservation planning, the variability of C stocks among wetland types and between above and belowground compartments requires further investigation. Additionally, the bathymetry of peat basins has often been ignored, with soil C stock calculations mostly relying on the thickest peat section, potentially leading to overestimates. Here, we sampled vegetation and soil of 57 wetlands of southeastern Canada to characterize the variability of above and belowground organic C stocks among four wetland types: open bogs, open fens, swamps, and forested peatlands. We also compared carbon stock estimation approaches considering peat bathymetry or not. Results showed that peat thickness, and thus soil organic C (SOC), varied substantially within sites due to peat basin shapes. Omitting bathymetry led to site-scale SOC overestimates of about 20–38 % on average, depending on the approach used, with wide variability among sites (overestimates up to 200 %). Belowground C stocks varied among wetland types with mean values of 132, 101, 19, and 44 kg C m−2 for bogs, fens, swamps, and forested peatlands, respectively. Aboveground C was nearly zero in open bogs and fens but reached ~30 % of total C stock in swamps and ~ 15 % in forested peatlands. C stocks in tree roots and shrubs were negligible. Despite the lower C density (per m2) of swamps and forested peatlands, these ecosystems represented the dominant C stocks at the regional scale due to their abundance in the landscape. Overall, the four wetland types stored an estimated 2–7 times more C than forest per unit area. Evaluating differences in C stocks according to wetland type, while integrating peat bathymetry in calculations, can significantly improve regional wetland conservation planning.
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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 | 0.001 | 0.001 |
| 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".