Impacts of active retrogressive thaw slumps on vegetation, soil, and net ecosystem exchange of carbon dioxide in the Canadian High Arctic
Bibliographic record
Abstract
Retrogressive thaw slumps (RTS) are permafrost disturbances common on the Fosheim Peninsula, Ellesmere Island, Canada. During the 2013 growing season, three different RTS were studied to investigate the impact on vegetation composition, soil, and growing season net ecosystem exchange (NEE) of CO2 by comparing to the adjacent undisturbed tundra. Eddy covariance and static chamber measurements were used to determine NEE and ecosystem respiration (Re), respectively. Vegetation cover was significantly lower in all active disturbances, relative to the surrounding tundra, and this affected the overall impact of disturbance on CO2 fluxes. Disturbances were characterized by greater Re compared to surrounding undisturbed tundra. Over the mid-growing season (34 days), eddy covariance NEE measurements indicated that there was greater net CO2 uptake in undisturbed versus disturbed tundra. At one site, the undisturbed tundra was a weak net sink (−0.05 ± 0.02 g C m−2 day−1), while the disturbed tundra acted as a weak net source (+0.07 ± 0.04 g C m−2 day−1). At the other site, the NEE of the undisturbed tundra was −0.20 ± 0.03 g C m−2 day−1 (sink), while the disturbed tundra still sequestered CO2, but less than the undisturbed tundra (NEE = −0.05 ± 0.04 g C m−2 day−1). Two of the RTS exhibited average soil temperatures that were greater compared to the surrounding undisturbed tundra. In one case, the opposite effect was observed. All RTS exhibited elevated soil moisture (+14%) and nutrient availability (specifically nitrogen) relative to the undisturbed tundra. We conclude that RTS, although limited in space, have profound environmental impacts by reducing vegetation coverage, increasing wet soil conditions, and altering NEE during the growing season in the High Arctic.
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 | 0.001 | 0.001 |
| 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".