Incorporating Ecosystem Services into Water Resources Management—Tools, Policies, Promising Pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ecosystems provide a range of services, including water purification, erosion prevention, and flood risk mitigation, that are important to water resource managers. But as a sector, water resources management has been slow to incorporate ecosystem protection and restoration, for a variety of reasons, although related concepts such as nature-based solutions and green infrastructure are gaining traction. We explain some of the existing challenges to wider uptake of the ecosystem services concept in water resources management and introduce some promising avenues for research and practice, elaborated in more detail through 12 papers, spanning five continents and a variety of contexts, which make up a Special Issue on "Incorporating Ecosystem Services into Water Resources Management". Cross-cutting themes include (A) ecosystem services as a flexible concept to communicate with stakeholders; (B) participatory processes to involve stakeholders in research; (C) multiple values, and valuation methods, of water-related services; and (D) applications of decision-support tools. We conclude with a summary of research gaps and emphasize the importance of co-producing knowledge with decision makers and other stakeholders, in order to improve water resources management through the integration of ecosystem services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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