Potential carbon loss associated with post-settlement wetland conversion in southern Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Natural wetlands can mitigate ongoing increases in atmospheric carbon by storing any net balance of organic carbon (peat) between plant production (carbon uptake) and microbial decomposition (carbon release). Efforts are ongoing to quantify peat carbon stored in global wetlands, with considerable focus given to boreal/subarctic peatlands and tropical peat swamps. Many wetlands in temperate latitudes have been transformed to anthropogenic landscapes, making it difficult to investigate their natural/historic carbon balance. The remaining temperate swamps and marshes are often treated as mineral soil wetlands and assumed to not accumulate peat. Southern Ontario in the Laurentian Great Lakes drainage basin was formerly a wetland-rich region that has undergone significant land use change since European settlement. RESULTS: This study uses southern Ontario as a case study to assess the degree to which temperate regions could have stored substantial carbon if it had not been for widespread anthropogenic land cover change. Here, we reconstruct the full extent and distribution of natural wetlands using two wetland maps, one for pre-settlement conditions (prior to 1850 CE) and the other for modern-day patterns of land use (2011 CE). We found that the pre-settlement wetland cover decreased by about 56% with the loss most significant for marshes as only 11% of predicted pre-settlement marshland area remains today. We estimate that pre-settlement wetlands held up to ~ 3.3 Pg of carbon relative to ~ 1.3 Pg for present-day (total across all wetland classes). CONCLUSIONS: By not considering the recent carbon loss of temperate wetlands, we may be underestimating the wetland carbon sink in the pre-industrial carbon cycle. Future work is needed to better track the conversion of natural wetlands globally and the associated carbon stock change.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".