Evidence for elevated emissions from high‐latitude wetlands contributing to high atmospheric CH<sub>4</sub>concentration in the early Holocene
Bibliographic record
Abstract
The major increase in atmospheric methane (CH4) concentration during the last glacial‐interglacial transition provides a useful example for understanding the interactions and feedbacks among Earth's climate, biosphere carbon cycling, and atmospheric chemistry. However, the causes of CH4doubling during the last deglaciation are still uncertain and debated. Although the ice‐core data consistently suggest a dominant contribution from northern high‐latitude wetlands in the early Holocene, identifying the actual sources from the ground‐based data has been elusive. Here we present data syntheses and a case study from Alaska to demonstrate the importance of northern wetlands in contributing to high atmospheric CH4concentration in the early Holocene. Our data indicate that new peatland formation as well as peat accumulation in northern high‐latitude regions increased more than threefold in the early Holocene in response to climate warming and the availability of new habitat as a result of deglaciation. Furthermore, we show that marshes and wet fens that represent early stages of wetland succession were likely more widespread in the early Holocene. These wetlands are associated with high CH4emissions due to high primary productivity and the presence of emergent plant species that facilitate CH4transport to the atmosphere. We argue that early wetland succession and rapid peat accumulation and expansion (not simply initiation) contributed to high CH4emissions from northern regions, potentially contributing to the sharp rise in atmospheric CH4at the onset of the Holocene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".