Coastal climate change vulnerability and adaptation in Fundy National Park, New Brunswick
Bibliographic record
Abstract
As global climate changes, coastal areas such as Fundy National Park in New Brunswick are projected to feel the effects of sea level rise and associated increase in storm surge.The purpose of this research was to determine the vulnerability of the Park's coastline to climate change impacts using field based and GIS assessments along 7km of coastline that was accessible overland.Current and future vulnerability of coastal assets were assessed under current conditions and climate change projections for 2050 and 2100 using ArcGIS 10.4 as a tool for visualization and analysis of projected sea level rise along the Park's coastline.Finally, the Atlantic Climate Adaptation Solutions Association (ACASA) Coastal Community Decision Tree Web Tool was used to assess options to adapt the coastline to identified vulnerabilities, and a specific adaptation plan was created through combined use of the web tool recommendations and local knowledge.It was found that of the assessed coastline, 47% of the backshore was stable or intact, 32% was partially stable or damaged, and 19% was unstable or failing.There was a direct correlation between the locations of some low-lying features with certain coastal assets, so these assets were deemed to be vulnerable, and adaptation options were explored for their particular locations.The coastline of Fundy National Park is a major tourist draw for the Park, so it is in the best interest of managers to create a climate change monitoring and adaptation plan to maintain the coastline for the safety and enjoyment of visitors into the future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".