Dynamic of Service Value of Farmland Meta-ecosystem of Mountain,Oasis and Desert and Multiple Regression Analysis of the Influence Factors in the Hexi Corridor,Gansu,China
Bibliographic record
Abstract
To analyze the ecosystem service value will help us to understand the function heterogeneity of the ecosystem service function from different ecological function regions.We took Sunan county,Ganzhou district,and Minqin county,three typical areas of the Mountain-Oasis-Desert(MOD)in the Hexi Corridor as an example.We calculated the farmland ecosystem service value in different ecological function regions in 2002 and 2009 by using the method of ecological economics,and analyzed the influence factors of the change of farmland ecosystem service value by using multiple regression.The results showed that:The farmland ecosystem service value per unit area of Hexi orridor was in an order of northern desert sub-ecosystemmiddle oasis sub-ecosystemsouthern mountain sub-ecosystem;The farmland ecosystem service value increased in different ecological function regions during the research period,such as the northern desert sub-ecosystem increased$1.83×108,the middle oasis sub-ecosystem increased$0.70×108,and the southern mountain subecosystem increased$0.13×108;The simple ecosystem service value increased about$0.16×108 per year in northern desert,increased about$0.11×108 per year in middle oasis and increased about$0.02×108 per year in southern mountain;For the use of fertilizers,the loss in the value of farmland ecosystem service increased in different ecological function regions;On the agricultural water consumption side,the loss value decreased about$0.11×108 per year in northern desert,increased about$0.03×108 per year in middle oasis,but changed smaller in south mountain;The results from regression analysis may reveal that the change of farmland ecosystem service value in Hexi Corridor mainly caused by growth in structural transformation,but the impact degree exist difference in different ecological functional areas.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".