Wetland succession in a permafrost collapse: interactions between fire and thermokarst
Bibliographic record
Abstract
Abstract. To determine the influence of fire and thermokarst in a boreal landscape, we investigated peat cores within and adjacent to a permafrost collapse feature on the Tanana River Floodplain of Interior Alaska. Radioisotope dating, diatom assemblages, plant macrofossils, charcoal fragments, and carbon and nitrogen content of the peat profile indicate ~600 years of vegetation succession with a transition from a terrestrial forest to a sedge-dominated wetland over 100 years ago, and to a Sphagnum-dominated peatland in approximately 1970. The shift from sedge to Sphagnum, and a decrease in the detrended tree-ring width index of black spruce trees adjacent to the collapse coincided with an increase in the growing season temperature record from Fairbanks. This concurrent wetland succession and reduced growth of black spruce trees indicates a step-wise ecosystem-level response to a change in regional climate. In 2001, fire was observed coincident with permafrost collapse and resulted in lateral expansion of the peatland. These observations and the peat profile suggest that future warming and/or increased fire disturbance could promote permafrost degradation, peatland expansion, and increase carbon storage across this landscape; however, the development of drought conditions could reduce the success of both black spruce and Sphagnum, and potentially decrease the long-term ecosystem carbon storage.
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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 | 0.000 | 0.000 |
| 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".