Simulations of the net biospheric carbon emissions of management peatlands, and evaluation of the impact of management strategies for meeting a carbon neutral point, and net zero targets.
Bibliographic record
Abstract
Peatlands cover approximately ~3 % of the global terrestrial surface (Joosten, 2009) and account for 11 – 14 % of Canadian land cover. Peatlands form and grow over millennia, taking up carbon from the atmosphere. Canadian peatlands store ~ 150 Pg C but are subject to disturbances that disrupt their carbon store. 350 km2 (0.03 %) of peatlands in Canada are disturbed through peat extraction for horticulture. While the area of peatlands disturbed for horticulture is small, the emissions caused by extraction are significant. I develop a systems model to look at the impact of peat extraction management, as well as the management of the fate of extracted peat, on net biospheric carbon emissions. The model was based on previous peatland simulation models and was evaluated using field measurements from Rivière-du-Loup in Eastern Québec, Canada. Sensitivity analysis showed that for each average year of extraction, the emissions from the field required up to 10 years of post-restoration uptake to offset. For every 1 kg C emitted per square metre as part of downstream emissions, an average of ~ 50 years would be needed to take up the biospheric carbon by the restored peatland. Scenarios suggest that cumulative field biospheric carbon emissions will be offset by restoration within 120 to 220 years. However, extracted peat remains in the biosphere until it is decomposed. The inclusion of downstream emissions (scope 3) suggests that it will take many millennia after successful restoration for the biosphere for cumulative carbon emissions to return to zero. I conclude that while peatlands are renewable on a short geological timescale, the emitted carbon is not recoverable on anthropogenic policy timescales
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".