Protecting ecosystem services and biodiversity in the world's watersheds
Bibliographic record
Abstract
Abstract Despite unprecedented worldwide biodiversity loss, conservation is not at the forefront of national or international development programs. The concept of ecosystem services was intended to help conservationists demonstrate the benefits of ecosystems for human well‐being, but services are not yet seen to truly address human need with current approaches focusing mostly on financial gain. To promote development strategies that integrate conservation and service protection, we developed the first prioritization scheme for protecting ecosystem services in the world's watersheds and compared our results with global conservation schemes. We found that by explicitly incorporating human need into prioritization strategies, service‐protection priorities were squarely focused on the world's poorest, most densely populated regions. We identified watersheds in Southeast Asia and East Africa as the most crucial priorities for service protection and biodiversity conservation, including Irrawaddy—recently devastated by cyclone Nargis. Emphasizing human need is a substantial improvement over dollar‐based, ecosystem‐service valuations that undervalue the requirements of the world's poor, and our approach offers great hope for reconciling conservation and human development goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".