Threats, Monitoring, and Policy to Present and Future Climate Change from Algonquin Park (Ontario, Canada) to the Adirondack Park (New York, United States)
Bibliographic record
Abstract
Anthropogenic greenhouse gas emissions have been steadily increasing since the Industrial Revolution. The release of greenhouse gases and the results in changes in global climate have made it a challenge for parks and protected areas to respond to the potential negative impacts to ecological integrity. The predicted rate of climate change is forecasted to be faster than the rate of deglacial warming and a fragmented landscape between large protected areas further contributes to our challenges. The Algonquin to Adirondack corridor provides a corridor for flora and fauna to migrate in the face of climate change. Assessing the perceived threats, current level of monitoring and assessment, and climate change policy provides the framework to assess our preparedness to adapt to climate change on study areas within the Algonquin to Adirondack corridor. To compile data, a literature review was completed and 8 individuals representing 7 governmental and non-governmental organizations were interviewed. The findings include: 1) there are concerns that climate change is affecting study areas, however, climate change is a large problem that many areas are not financially or capacity-wise able to deal with; 2) monitoring and assessment relevant to climate change is occurring within study areas but no standardized method is utilized; 3) budget cuts for all organizations is impacting the ability to accomplish continuous data collection, however, citizen science may potentially fill this gap; 4) there are no specific climate change policies for parks and adjacent regions. The main policy recommendation based on this research is to employ an adaptive management approach to take into account the unpredictable nature of our climate future. Additionally, given the board range of climate change impacts, tackling this issue can be done quicker and more effectively when accomplished strategically and using partnerships across this region.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".