Assessing Estuary Resilience to Sea-Level Rise
Bibliographic record
Abstract
Estuaries and coastal wetlands comprise less than 3% of BC’s coastline, yet they support over 80% of BC’s coastal fish and wildlife, and provide critical rearing and staging habitat for Pacific salmon. Estuary ecosystems are particularly sensitive to the impacts of sea-level rise. Fine-scale changes in water depth can result in the drowning of tidal marsh habitats or significant changes to vegetation community composition. Not all estuaries are equally vulnerable however; the most resilient receive adequate sediment or build soils in pace with sea-level rise, while others may have been disconnected from the rivers that deliver their natural sediment supply. The U.S. National Estuarine Research Reserve System (NERRS) has developed the Marsh Resilience to Sea-Level Rise (MARS) tool - a monitoring approach designed to assess and rank the vulnerability of estuaries to sea-level rise. The NERRS undertook a large study in which 16 sites across the U.S. were assessed and ranked. In 2019, The Nature Trust of British Columbia (NTBC), in partnership with the West Coast Conservation Land Management Program (WCCLMP) and Coastal First Nations, secured a contribution agreement under The BC Salmon Restoration and Innovation Fund (BC SRIF) to implement their five year project, entitled Enhancing Estuary Resilience: An Innovative Approach to Sustaining Fish and Fish Habitat in a Changing Climate. The NTBC’s monitoring program is implementing the MARS tool at 15 estuaries along the coast of BC, extending the coverage of the NERRS study northwards along the west coast of North America, providing a Canadian context.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".