Ecological security evaluation of grassland based on combined weight on principle of relative entropy: A case of Yellow River water source areas of Gannan
Bibliographic record
Abstract
Focusing on the P-S-R model and based on Combined Weight using relative Entropy principle,an integrated assessment model was constructed to assess the regional ecological security in the Yellow River Water Supply Areas of Gannan.In this model,the indexes weights were computed by using Analytic Hierarchy Process and entropy principle,and the combination weight can be synthesized according to minimum relative information entropy principle.In order to realize the evaluation function of single index evaluation process,a new evaluation method of single index was proposed by using attribute recognition model.And then the new evaluation method,named AR-CWE for short,was established with the support of combined weight and the single index evaluation.The results showed that the whole situation of ecological security of Gannan was bad;the towns whose ecological security was the worst were the pure pasturing area;the towns whose ecological security was worse were the semi-planting-pasturing area;the towns whose ecological security were generally the agricultural area.Through the analysis of ecological security situation in the Yellow River water source areas of Gannan,we found that the changes of annual air temperature and annual precipitation,the difference way of farmer's income,the educational level of herds,the overgrazing and the rate of grassland degradation were the main factors which had a great effect on the ecological security in the Yellow River water source areas of Gannan.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".