Using systematic review to analyse climate change impacts and adaptation associated with mountain permafrost
Bibliographic record
Abstract
Mountain permafrost in Asia incorporates permafrost in the mountains of the Hindu Kush Himalayan region, Central Asia, Russia, Mongolia, Qinghai Tibetan plateau and other mountain ranges in China. Changes in climate variables in recent decades have considerably influenced permafrost in these regions and produced vivid impacts. While climate change impacts on mountain permafrost in the alpine regions of Europe, US and Canada are relatively well documented, records about mountain permafrost in Asia are mostly available for the Qinghai Tibetan plateau region and a few other mountain ranges in China. Considerably little information is available for the Hindu Kush Himalayan region and other mountain ranges in Asia. This systematic review analyses climate change related impacts and adaptation in mountain permafrost regions of Asia and attempts to evaluate the status of knowledge based on peer-reviewed journal publications. Impacts on hydrology, geomorphology and ecology were examined and resulting socioeconomic effects were considered. Additionally, ongoing and potential adaptation practices were explored. Warming climate has been found responsible for a gradual shift of the lower limit of mountain permafrost in the region. Increased probabilities of mass wasting events due to reduced slope stability, changes in composition and quality of fresh water resources, irregularities in seasonal flows, changes in permafrost ecosystems and contemporaneous need for the protection of engineered constructions were identified as some of the key impacts. There is a high necessity for increased understanding of mountain permafrost and well-designed response actions to evaluate processes and interactions influencing changes in the natural environment and subsequent effects on sustainable living conditions. Therefore, suitable risk management practices need to be designed with a proper consideration of the anticipated future dynamics of climate, economy and society.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".