Assessing the Influence of Climate and Land-Use Change on Jefferson Salamanders in Ontario's Greenbelt
Bibliographic record
Abstract
The Golden Horseshoe of Ontario is one of the fastest growing regions in North America, with a projected population of over 13.5 million people by 2041. Over 2 million acres of land within this area is protected by the Greenbelt, a framework created to promote conservation and prevent urban sprawl within ecologically significant and sensitive lands. However, habitat fragmentation due to human development, coupled with shifting climate patterns poses a significant threat to species at risk, including the Jefferson Salamander (Ambystoma jeffersonianum). This species is particularly vulnerable to habitat fragmentation, which reduces access to vernal ponds that are essential for breeding. This study aims to address how climate and land-use changes have impacted the range of Jefferson Salamanders in the Ontario Greenbelt. To assess changes between historical and current salamander distributions, provincially tracked occurrence data is mapped alongside Greenbelt boundaries and designations. Land-use changes between 2000 and 2015 in the area are quantified using the Southern Ontario Land Resource Information System (SOLRIS) Versions 1.1 and 3. Additionally, historical climate data from Environment and Climate Change Canada is analyzed to evaluate shifts in temperature and precipitation trends. Early results indicate a high concentration of the Jefferson Salamander throughout the Niagara Escarpment, with populations bordering or within high-density areas of the Greater Toronto Area (GTA). Climate normals from the 10 weather stations within the Greenbelt boundary illustrate an overall increase in temperature and precipitation trends from the period 1961-1990 to 1981-2010. These findings suggest that habitat fragmentation and shifts in local climate may be contributing to a reduction in range and suitable habitats of the Jefferson Salamander.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".