How Geographic Information Systems Can Help Roosevelt Campobello International Park With Climate Mitigation and Adaptation Procedures
Bibliographic record
Abstract
Geographic Information Systems (GIS) are used by U.S. and Canadian National Parks for various projects including land use planning, ecosystem monitoring, and climate change monitoring. How can a park like Roosevelt Campobello International Park (RCIP), which is jointly managed between the U.S. and Canada, use GIS to help with climate change mitigation and adaptation? Trail data and inventorying of the park’s bog walk infrastructure were collected over the summer of 2024. Land cover data was used from Natural Resource Canada for three time periods: 2010, 2015, and 2020. Carbon data was found using the International Panel for Climate Change 2006 and 2019 referendum, then combined with the land cover data to estimate carbon storage and change for two different time intervals (2010–2015) and (2015–2020). A weighted analysis was conducted, and a network analysis was run. A combined trail network map and bog walk inventory map was created, highlighting the changes to the park’s trail system, carbon storage for 2010 and 2015, the importance of wetlands as a sink, and change in carbon storage for each year from 2015–2020, showing the changes in land cover and its effect on carbon storage for RCIP. A flood extent map was created, highlighting the areas of the park most at risk for flooding during a high precipitation event, and a proposed bus route was created to convince the park to adopt a zero-emission shuttle service. Using Geographic Information Systems allows RCIP to make data-driven decisions to help with conservation, enhance park infrastructure, and build climate resilience for the park.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".