Resilience and vulnerability of permafrost to climate changeThis article is one of a selection of papers from The Dynamics of Change in Alaska’s Boreal Forests: Resilience and Vulnerability in Response to Climate Warming.
Bibliographic record
Abstract
The resilience and vulnerability of permafrost to climate change depends on complex interactions among topography, water, soil, vegetation, and snow, which allow permafrost to persist at mean annual air temperatures (MAATs) as high as +2 °C and degrade at MAATs as low as –20 °C. To assess these interactions, we compiled existing data and tested effects of varying conditions on mean annual surface temperatures (MASTs) and 2 m deep temperatures (MADTs) through modeling. Surface water had the largest effect, with water sediment temperatures being ~10 °C above MAAT. A 50% reduction in snow depth reduces MADT by 2 °C. Elevation changes between 200 and 800 m increases MAAT by up to 2.3 °C and snow depths by ~40%. Aspect caused only a ~1 °C difference in MAST. Covarying vegetation structure, organic matter thickness, soil moisture, and snow depth of terrestrial ecosystems, ranging from barren silt to white spruce ( Picea glauca (Moench) Voss) forest to tussock shrub, affect MASTs by ~6 °C and MADTs by ~7 °C. Groundwater at 2–7 °C greatly affects lateral and internal permafrost thawing. Analyses show that vegetation succession provides strong negative feedbacks that make permafrost resilient to even large increases in air temperatures. Surface water, which is affected by topography and ground ice, provides even stronger negative feedbacks that make permafrost vulnerable to thawing even under cold temperatures.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".