Landscape‐scale variations in near‐surface soil temperature and active‐layer thickness: Implications for high‐resolution permafrost mapping
Bibliographic record
Abstract
Abstract Soil temperature observations in permafrost regions are sparse, which limits our understanding and ability to map permafrost conditions at high spatial resolutions. In this study, we measured near‐surface soil temperatures (Tnss) at 107 sites from August 2016 to August 2017 in northern boreal and tundra areas in northwestern Canada. Active‐layer thickness (ALT), soil and vegetation conditions were also measured at these sites. Our observations show large variations in Tnss and ALT across an area with a similar climate. This high degree of spatial heterogeneity illustrates the importance of high‐resolution mapping of permafrost for infrastructure planning and understanding the impacts of permafrost thaw. Annual mean Tnss varied by 5–6°C among observation sites, which was mainly due to differences in Tnss in winter and spring, indicating the importance of snow conditions on determining landscape‐scale variation in near‐surface ground temperatures. ALT varied from about 30 cm to more than 120 cm. The variation in ALT among sites did not correlate with thawing season Tnss, but was associated with variation in soil conditions, especially the surface organic layer thickness. Freezing n‐factors varied significantly from site to site and among ecotypes, while thawing n‐factors were similar among sites, except bare soils. This study shows that ecotypes can be used to map ALT and Tnss at landscape scales in tundra areas, but the method is not as effective in the northern boreal region.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".