Integrating public preferences with biophysical production possibilities: an application to ecosystem services from dam removal
Bibliographic record
Abstract
Effective management of ecosystem services requires understanding the biophysical relationships governing the trade-offs, as well as stakeholder preferences for the trade-offs. However, useful tools to guide the complex decision-making process are often lacking. This study demonstrates an approach that combines biophysical and economic models to identify socially preferred solutions. We demonstrate in the context of dam-removal decisions across thousands of dams in Maine, U.S. The results demonstrate the practical usability of this framework for identifying key trade-offs, areas in which people are in agreement and conflicted, along with solutions that are more preferred by society overall. The results also reveal a 30–47% welfare gain from optimizing across all ecosystem services, compared to a more common, visual approach of optimizing two services at a time. This approach may be useful to identify restoration projects that are likely to garner broad public support, particularly when there are trade-offs between ecosystem services, numerous potential solutions, and communities with diverging preferences.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".