Effect of Vegetation Cover on the Ground Thermal Regime of Wooded and Non‐Wooded Palsas
Bibliographic record
Abstract
ABSTRACT Although warming air temperatures are contributing to permafrost degradation across the circumpolar zone, understanding of permafrost and environmental feedbacks to climate change is limited. Palsas can be used as indicators of permafrost stability given their sensitivity to changes in temperature and precipitation. However, field observations on the effects of vegetation cover are needed to compare permafrost dynamics of wooded and non‐wooded palsas. This study examined the influence of vegetation on the soil thermal regime of wooded palsas covered by black spruce trees and non‐wooded palsas covered by shrubs in discontinuous permafrost of the Boniface River area of northern Quebec, Canada. It investigated the effects of organic layer thickness, vegetation and snow depth on soil temperature at 50 cm and 100 cm depths for over 2 years. The coldest summer soil temperatures were associated with thick organic layers. In summer, soil temperatures were colder under spruce stands than under shrub canopies and forest openings, whereas the thick snow cover in spruce stands and forest openings maintained warmer winter soil temperatures than under shrub canopies. Well‐defined zero‐curtain periods during fall and spring could be an early indicator of current changes in the soil thermal regime of palsas. At the northern edge of discontinuous permafrost, non‐wooded palsas have the most favourable conditions for permafrost stability, because heterogeneous vegetation cover on wooded palsas promotes snow trapping and lateral heat transfer. Vegetation types should be considered in estimating future rates of permafrost degradation. Copyright © 2014 John Wiley & Sons, Ltd.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".