Climate warming and active layer thaw in the boreal and tundra environments of the Mackenzie Valley
Bibliographic record
Abstract
The variability of maximum active layer thickness in boreal and tundra environments has important implications for hydrological processes, terrestrial and aquatic ecosystems, and the integrity of northern infrastructure. For most planning and management purposes, the long-term probability distribution of active layer thickness is of primary interest. A robust method is presented to calculate maximum active layer thickness, employing the Stefan equation to compute phase change of moisture in soils and using air temperature as the sole climatic forcing variable. Near-surface ground temperatures (boundary condition for the Stefan equation) were estimated based on empirical relationships established for several sites in the Mackenzie valley. Simulations were performed for typically saturated mineral soils, overlain with varying thickness of peat in boreal and tundra environments. The probability distributions of simulated maximum active layer thickness encompass the range of measured thaw depths provided by field data. The effects of climate warming under A2 and B2 scenarios for 2050 and 2100 were investigated. Under the A2 scenario in 2100, the simulated median thaw depth under a thin organic cover may increase by 0.3 m, to reach 1 m depth for a tundra site and 1.6 m depth for a boreal site. The median thaw depth in 2100 is dampened by about 50% under a 1 m thick organic layer. Without an insulating organic cover, thaw penetration can increase to reach 1.7 m at the tundra site. The simulations provide quantitative support that future thaw penetration in permafrost terrain will deepen differentially depending on location and soil.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".