Simulating the exchange of carbon in Canadian pristine, disturbed and restored peatlands
Bibliographic record
Abstract
Approximately 1.19 x 106 km2 of Canada is covered by peatlands containing over 110 to 150 Gt C. Most are relatively pristine. A tiny area (~ 0.21 x 106 ha) of Canadian peatlands is affected by land-use change. The most common land disturbances are due to agriculture, fossil fuel and mineral exploration and extraction, and the creation of hydroelectric reservoirs. A small area of peatlands (~350 km2) is disturbed by peat extraction for use in horticulture. We have simulated the emissions of CO2 from pristine, extractive and restored peatlands using the Coupmodel. Coupmodel reproduces the exchanges of energy, water and carbon well for pristine peatlands and shows their sensitivity to changes in water storage. We have also successfully simulated the emissions from peatlands that are undergoing extraction. Our results show that extraction converts a peatland from a sink of ~ 20 to 100 g C m-2 yr-1 to a source of ~ 150 – 200 g C m-2 yr-1. Finally, we have simulated peatlands that have been restored using ecological approaches (e.g. the moss transfer technique). They return to being a sink in the same range of undisturbed peatlands 14 years after restoration. The sink strength is a function of water table depth. Simulations also show that the restored peatlands are relatively insensitive to climate change over the projected conditions for the next one hundred years. The key to successfully simulating the carbon dynamics of pristine and disturbed peatlands is to be able to simulate the hydrological and thermal conditions well. We demonstrate Coupmodel’s capabilities against measurements from pristine, disturbed and restored peatlands. Simulating the biogeochemistry beyond the range of measurements can provide insight for emissions accounting, climate-smart management, and land-use 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".