The Influences of Land Use and Economic Policy on Main Ecosystem Services in Rural East China
Bibliographic record
Abstract
The growing need for food provision and materials challenges the maintenance of ecosystem services. Understanding the composition of ecosystem services and the factors that affect the services are critical to improving rural development. An assessment of ecosystem services in the densely populated rural areas of East China has been conducted. The results show the average value of rural ecosystem services was 34.99 thousand RMB/ha. The average value of provision services was 30.01 thousand RMB/ha, which was the main part of ecosystem services. The relationships between provision services and ecosystem services were complex. Provision (nutrition) services had no significant correlation with regulation services and provision (material) services. Provision services were mainly influenced by forest cover, proportion of arable land, and rural population (adjusted R2 = 0.36). Social factors and land use factors also had a significant impact on nutrition provision services and material provision services. Land and economic policies could regulate the rural ecosystem service value by changing land use types, population mobility, and rural income. Our findings may shed light on the synergetic development of ecosystem services, provision services, and village development in densely populated rural areas worldwide.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".