Using ecosystem service trade‐offs to inform water conservation policies and management practices
Bibliographic record
Abstract
Environmental managers and policy makers are increasingly discussing trade‐offs between ecosystem services, but few studies have analyzed these trade‐offs with a view to informing land‐use planning. Using specialized models, we quantify ecosystem services in several land‐use scenarios relative to actual land‐use change over a 9‐year period. These scenarios were developed in an effort to maintain agricultural production while improving water quality and increasing water quantity in the watershed of the Miyun Reservoir, the only source of surface water currently available for domestic use in Beijing, China. Within the watershed, from 2000 to 2009, forest cover and urban area increased by 33% and 280%, while water provision and water purification services declined by 9% and 27%, respectively. Under a hybrid scenario of agricultural expansion with riparian grassland buffers, three services – water provision, water purification, and sediment retention – as well as agricultural production all improved as compared with 2009 levels. Riparian grassland protection zones, seldom used in China, can effectively resolve trade‐offs among multiple ecosystem services and are now being considered and implemented in several locations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".