Ecological security evaluation of grassland based on combined weight on principle of relative entropy: A case of Yellow River water source areas of Gannan
Why this work is in the frame
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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.
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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 | 0.002 | 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 | 0.000 |
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 it