Community-based mapping of potential vernal pools using LiDAR in South-Central Ontario
Bibliographic record
Abstract
Vernal pools are essential breeding habitat for amphibians - the vertebrates most at-risk across the globe. Unfortunately, due to their small sizes and temporary nature, vernal pools are prone to indiscriminate destruction. This is the case in southern Ontario as most vernal pools have already been destroyed by human development. As such there is an urgent need to map remaining vernal pools in relatively undeveloped forested regions, such as the District Municipality of Muskoka in South-Central Ontario. This thesis aims to head-start the creation of a community-based vernal pool mapping project using LiDAR in South-Central Ontario. This goal has been broken down into two chapters with their own sub-objectives. In one chapter, we implemented a pilot study for integrating community involvement in potential vernal pool mapping across the Muskoka River Watershed (i.e., the major watershed of the District of Muskoka). We built a protocol and survey based on past vernal pool projects and studies which effectively integrated citizen involvement and also implemented novel online components (e.g., a portal) for vernal pool field-work. Our efforts were successful with positive feedback for the online components and a majority of the potential vernal pools located by our volunteers were probable vernal pools. In the other chapter, we developed two potential vernal pool mapping protocols using LiDAR based on regional characteristics of pools across the District of Muskoka in the Muskoka River Watershed and Coastal Georgian Bay. We demonstrated that the best mapping protocol for each of the two regions were associated with the protocol that was based on their respective pool characteristics. Moreover, we determined that while LiDAR can increase the accuracy of vernal pool mapping efforts, this is not always the case, especially when mapping vernal pools that occur in expansive bedrock laden regions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".