Control of Short‐Stature Vegetation Type on Shallow Ground Temperatures in Permafrost Across the Eastern Canadian Arctic
Bibliographic record
Abstract
Abstract The Arctic has warmed three times the rate of the global average, resulting in extensive thaw of perennially frozen ground known as permafrost. While it is well understood that permafrost thaw will continue and likely accelerate, thaw rates are nonuniform due, in part, to the expansion of Arctic trees and tall shrubs that may increase ground temperatures. However, in permafrost regions with short‐stature vegetation (height < 40 cm), our understanding of how ground temperature regimes vary by vegetation type is limited as these sites are generally found in remote high‐latitude regions that lack in situ ground temperature measurements. This study aims to overcome this limitation by leveraging in situ shallow ground temperatures, remote sensing observations, and topographic parameters across 22 sites with varying types of short‐stature vegetation on Baffin Island, Canada, a remote region underlain by rapidly warming continuous permafrost. Results suggest that the type of short‐stature vegetation does not necessarily correspond to a distinct shallow ground temperature regime. Instead, in permafrost regions with short‐stature vegetation, factors that control snow duration, such as microtopography, may have a larger effect on evolving ground temperature regimes and thus permafrost vulnerability. These findings suggest that anticipating permafrost thaw in regions of short‐stature vegetation may be more nuanced than previously suggested.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".