Participatory mapping of ecosystem services to understand stakeholders’ perceptions of the future of the Mactaquac Dam, Canada
Bibliographic record
Abstract
Rebuilding or removing a dam at the end of its lifespan will change provision of and access to ecosystem services. Understanding such changes involves assessing their biophysical provision, economic value and social demand, of which the latter is often neglected. We used participatory mapping to understand the spatial distribution of social benefits from ecosystem services around the Mactaquac Dam, New Brunswick, Canada, and assessed whether perceptions of ecosystem services under future scenarios can be mapped. We asked 32 participants to map places that were important to them for several ecosystem services, and asked how those places and services would change if the dam were rebuilt or removed. Participants benefitted from services throughout the reservoir, downstream of the dam, and in unaffected tributaries. Those who preferred to rebuild the dam mapped places in and around the reservoir, while those who wanted to remove it preferred the tributaries and downstream reach. Most participants could not map service distribution if the dam were removed, but could describe non-place-specific changes. Participatory mapping is useful for understanding how and where stakeholders benefit from ecosystem services, and to prompt discussion of perceived future changes. It is less useful for producing maps of ecosystem services under various scenarios.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".