Climate-Change Induced Permafrost Degradation in Yakutia, East Siberia
Bibliographic record
Abstract
Current climate change in the northern regions is a well-recognized phenomenon. In central Yakutia (the Sakha Republic), the long-term trend displays a consistent mean annual air temperature (MAAT) increase from −9.6˚C (1980) to −6.7˚C (2019), corresponding to an average 0.07˚C annual rise, with pronounced temperature anomalies in the last decade. The analyzed meteorological records of the past 40 years indicate a progressing climate change pattern of increased MAAT and mean annual precipitation (MAP) that occurs in 5 – 7 yr cycles. The complex interactions of regional climatic variations with local geological and environmental conditions influence the frozen ground’s thermal balance, which, in turn, impacts thermokarst development. Co-acting factors of temperature rise and higher precipitation rates activate thermokarst lake dynamics and lake expansion following snow- and rainfall-rich preceding years. April experiences the greatest warming trend with a present (2020) 5˚C rise from 1980 with shortening of the winter season. Climate warming together with natural forest fires and anthropogenic activities (pastoral practices and logging) contribute to the taiga landscape opening due to reduced albedo and the greater exposure to solar radiation. The regional hydrologic network undergoes restructuring caused by drained meltwater released from the degraded cryolithozone with peaks of the fluvial discharge in late spring and early summer generating bank erosion. The negative effects of the progressing ground thaw, which are particularly observed in lowland locations, pose risks to local settlements and generate major environmental and engineering problems in the formerly permafrost-stable central and northern areas of Siberia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".