Ecological risk assessment and ecological restoration zoning based on nature-society-landscape systems: a case study of Hexi Corridor, Northwest China
Bibliographic record
Abstract
Ecological risk is the core issue in achieving sustainable development, especially in ecologically fragile region like the Hexi Corridor. In this paper, under the multidimensional framework of ‘nature-society-landscape’, spatial principal component analysis is used to reveal its spatial and temporal evolution characteristics; hot spot clustering analysis is introduced to delineate the priority areas for ecological restoration; and combined with the geographic detector model to explore the main risk driving factors. The results show a spatial distribution pattern of ‘low in the southeast and high in the northwest’, and between 2000 and 2020, the risk level of Zhangye and Wuwei increased significantly, while the risk of Minqin, Dunhuang, and the Shule River Basin decreased. Cold spot areas in the southern foothills of the Qilian Mountains and the southeastern oasis area are designated as ecological development zones; hot spot areas in the northwestern part are recognized as priority restoration zones; and non-significant areas in the northern part of the Qilian Mountainsare classified as ecological protection zones. Habitat quality, vegetation coverage and annual precipitation are key drivers, with habitat quality having the strongest explanatory power. The framework provides a scientific basis for the ecological restoration management of the Hexi Corridor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".