The analysis and evaluation on grassland ecosystem service function value of Shandan Horse Field in Qilian Mountain National Nature Reserve
Bibliographic record
Abstract
The evaluation on grassland ecosystem service function value based on maintaining the structure,characteristics and processes plays an important role in formulating reasonable regional ecological economic development decision,protecting and restoring the grassland ecosystem,improving the consciousness of the environment resource.The 8 ecosystem services values of grassland of Shandan Horse Field in Qilian Mountain National Nature Reserve were assessed and evaluated by the market value method,the reforestation cost method,the shadow engineering method,replacement cost method and other methods.The result shows that the annual ecosystem service function value of the grassland in Shandan Horse Field is about 58.024 ×107 Yuan,among them the products providing function value is 1.840 × 107 Yuan;the regulating function value is 56.160×107 Yuan;the cultural function value is 0.0240×107 Yuan.Of all the value contributions of various service functions,water source conservation and climate regulation hold the largest proportion,which accounted for 45.481% and 39.277% respectively of all values.Therefore,protecting the grassland ecosystem of Shandan Horse Field is of the great significance to curb the deterioration of the ecological environment on North Slope of Qilian Mountains and maintain ecological safety of 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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".