Characteristics, impacts, and future research directions of Mongolian peatlands
Bibliographic record
Abstract
Mongolia is an important peatland distribution area in the world. Over the past few decades, Mongolian peatlands have undergone significant degradation due to climate change and human activities, yet there remains substantial uncertainty about the impacts of Mongolian peatland changes on regional environment and human society. Here, compiling the data of peatland distribution, climate, human activity, as well as the permafrost, we systematically review the distribution, changes, and the ecosystem service values of the Mongolian peatlands in the face of climate warming and intensifying human activities. The current data show that the total area of peatland in the Mongolian is 15 500–27 000 km 2 , and most of the peatlands are distributed in permafrost regions, while the accuracies of these maps were not assessed. In addition to climate warming, overgrazing, mining activities, and transportation, we suggest permafrost degradation also poses significant threatens on the peatlands. Although the importance of Mongolian peatlands has been recognized, the ecosystem service values, including water provision, habitat quality, carbon fixation, soil conservation, and wind erosion prevention largely remain unknown. Currently, efforts have been made to protect the Mongolian grassland, but there are no specific measures to combat peatland degradation. To better understand the changes and roles of peatland ecosystems in regulating regional development, we propose three research directions for future studies: (1) produce a detailed map of peatland distribution based on field survey data, new remote sensing data, and new algorithms; (2) unveil the mechanisms underlying the interaction of peat, vegetation, and permafrost; (3) evaluate the ecosystem services of Mongolian peatlands. These knowledges are the scientific foundation to propose and implement measures to protect, maintain, and sustainably utilize peatlands in Mongolia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".