Predicted Vulnerability of Carbon in Permafrost Peatlands With Future Climate Change and Permafrost Thaw in Western Canada
Bibliographic record
Abstract
Abstract Climate warming in high‐latitude regions is thawing carbon‐rich permafrost soils, which can release carbon to the atmosphere and enhance climate warming. Using a coupled model of long‐term peatland dynamics (Holocene Peat Model, HPM‐Arctic), we quantify the potential loss of carbon with future climate warming for six sites with differing climates and permafrost histories in Northwestern Canada. We compared the net carbon balance at 2100 CE resulting from new productivity and the decomposition of active layer and newly thawed permafrost peats under RCP8.5 as a high‐end constraint. Modeled net carbon losses ranged from −3.0 kg C m−2 (net loss) to +0.1 kg C m−2 (net gain) between 2015 and 2100. Losses of newly thawed permafrost peat comprised 0.2%–25% (median: 1.6%) of “old” C loss, which were related to the residence time of peat in the active layer before being incorporated into the permafrost, peat temperature, and presence of permafrost. The largest C loss was from the permafrost‐free site, not from permafrost sites. C losses were greatest from depths of 0.2–1.0 m. New C added to the profile through net primary productivity between 2015 and 2100 offset ∼40% to >100% of old C losses across the sites. Differences between modeled active layer deepening and flooding following permafrost thaw resulted in very small differences in net C loss by 2100, illustrating the important role of present‐day conditions and permafrost aggradation history in controlling net C loss.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".