Impacts of shrub removal on snow and near-surface thermal conditions in permafrost terrain adjacent to the Dempster Highway, NT, Canada
Bibliographic record
Abstract
The Peel Plateau, NT, Canada, is an area underlain by warm continuous permafrost where changes in soil moisture, snow conditions, and shrub density have increased ground temperatures next to the Dempster Highway. In this study, ground temperatures, snow, and thaw depth were monitored before and after tall shrub removal (2014). A snow survey after tall shrub removal indicated that snow depth decreased by a third and lowered winter ground temperatures when compared with control tall shrub sites. The response of ground temperatures to shrub removal depended on soil type. The site with organic soils had cooler winter temperatures and no apparent change in summer temperatures following shrub removal. At sites with mineral soil, moderate winter ground cooling insufficiently counteracted increases in summer ground heat flux caused by canopy removal. Given the predominance of mineral soil along the Dempster, these observations suggest tall shrub removal is not a viable short-term permafrost management strategy. Additionally, the perpendicular orientation of the Highway to prevailing winter winds stimulates snow drift formation and predisposes the site to warmer permafrost temperatures, altered hydrology, and tall shrub proliferation. Subsequent research should explore the effectiveness of tall shrub removal at sites with colder winter conditions or different snow accumulation patterns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".