Post-thaw variability in litter decomposition best explained by microtopography at an ice-rich permafrost peatland
Bibliographic record
Abstract
Litter decomposition, a key process by which recently fixed carbon is lost from ecosystems, is a function of environmental conditions and plant community characteristics. In ice-rich peatlands, permafrost thaw introduces high variability in both abiotic and biotic factors, both of which may affect litter decomposition rates in different ways. Can the existing conceptual frameworks of litter decomposition and its controls be applied across a structurally heterogeneous thaw gradient? We investigated the variability in litter decomposition and its predictors at the Stordalen subarctic peatland in northern Sweden. We measured in situ decomposition of representative litter and environments using litter bags throughout two years. We found highly variable litter decomposition rates with turnover times ranging from five months to four years. Surface elevation was a strong correlate of litter decomposition across the landscape, likely as it integrates multiple environmental and plant community changes brought about by thaw. There was faster decomposition but also more mass remaining after two years in thawed areas relative to permafrost areas, suggesting faster initial loss of carbon but more storage into the slow-decomposing carbon pool. Our results highlight mechanisms and predictors of carbon cycle changes in ice-rich peatlands following permafrost thaw.
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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".