Understanding multifunctional Bay of Fundy dykelands and tidal wetlands using ecosystem services—a baseline
Bibliographic record
Abstract
We review what is known about ecosystem service (ES) delivery from agricultural dykelands and tidal wetlands around the dynamic Bay of Fundy in the face of climate change and sea-level rise, at the outset of the national NSERC ResNet project. Agricultural dykelands are areas of drained tidal wetland that have been converted to agricultural lands and protected using dykes and aboiteaux (one-way drains or sluices), first introduced by early French settlers (Acadians). Today, Nova Scotia’s 242 km system of dykes protect 17,364 ha of increasingly diverse land uses—including residential, industrial, and commercial uses as well as significant tourism, recreational, and cultural amenities—and is undergoing system modernization and adaptation. Different ES are provided by drained and undrained landscapes such as agriculture from dykelands and regulating services from wetlands, but more complex dynamics exist when beneficiaries are differentiated. This review reveals many knowledge gaps about ES delivery and dynamics, including around net greenhouse gas implications, storm protection, water quality, fish stocks, pollination processes, sense of place, and aesthetics, some of which may reveal shared ES or synergies instead of trade-offs. We emphasize the need to be open to adapting ES concepts and categorizations to fully understand Indigenous implications of these land use decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".