Application of Ecological Technologies and Effects Evaluation in Typical Grassland Degradation Areas in the Headwater Region of the Yangtze River in China
Bibliographic record
Abstract
Grasslands are the most critical ecosystem on the Qinghai-Tibet Plateau. Their effective protection and restoration are vital for maintaining regional environmental stability and ensuring national ecological security. However, intensified global climate change and increasing human activity have significantly impacted the fragile grassland ecosystems of the headwater region of the Yangtze River, leading to severe degradation. This study integrates data from literature reviews, field surveys in representative areas, and stakeholder questionnaires. It aims to identify grassland degradation patterns, evaluate the characteristics and effectiveness of current ecological restoration technologies, and assess regional demand for ecological solutions. Key findings include: (1) Four distinct types of grassland degradation were identified, primarily driven by natural factors (e.g., climate change, rodent infestation) and human activities (e.g., overgrazing, livestock overloading). (2) Ecological technologies applied across the four degraded areas were assessed across five dimensions: application difficulty, promotion potential, maturity, suitability, and benefits. A deviation degree score quantified technology performance and restoration effectiveness. Fencing and enclosure, ecological compensation, and species selection emerged as the most effective technologies. Ecological migration and artificial rodent control faced significant implementation challenges. (3) Based on implementation outcomes, the study identifies current challenges and future requirements for ecological technologies tailored to specific degraded areas. This research provides valuable case studies for addressing grassland degradation in fragile ecosystems and offers a scientific basis for advancing sustainable grassland use and enhancing regional ecological carrying capacity.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".