Influence of Permafrost Type and Site History on Losses of Permafrost Carbon After Thaw
Bibliographic record
Abstract
Abstract We quantified permafrost peat plateau and post‐thaw carbon (C) stocks across a chronosequence in Interior Alaska to evaluate the amount of C lost with thaw. Macrofossil reconstructions revealed three stratigraphic layers of peat: (a) a base layer of fen/marsh peat, (b) peat from a forested peat plateau (with permafrost), and (c) collapse‐scar bog peat (at sites where permafrost thaw has occurred). Radiocarbon dating revealed that peat initiated within the last 2,500 years and that permafrost aggraded during the Little Ice Age (ca. 250–575 years ago) and degraded within the last several decades. The timing of permafrost thaw within each feature was not related to thaw bog size. Their rate of expansion may be more influenced by local factors, such as ground ice content and subsurface water inputs. We found C losses due to thaw over the past century were up to 46% of the C available, but the absolute amount of C lost (kg m −2 ) was over 50% lower than losses previously described in other Alaskan peatland chronosequences. We hypothesize that this difference stems from the process by which permafrost aggraded, with sites that formed permafrost epigenetically (significantly later than most peat accumulation) experiencing less absolute C loss with thaw than sites that formed syngenetically (simultaneously with peat accumulation). Epigenetic peat from our site had lower C:N ratios as compared to Alaskan sites that have syngenetic peat. This difference could help predict the magnitude of C loss with thaw across a range or permafrost types and histories.
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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 | 0.000 |
| 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".