Novel Techniques for Monitoring Freshwater Turtles in Ontario
Bibliographic record
Abstract
Freshwater turtles are struggling for their survival worldwide due to habitat loss and degradation, road mortality and illegal collection. To protect such at-risk species, we must establish accurate long-term monitoring programs to track changes in population abundance. For elusive freshwater turtles, tracking them requires trained researchers, specialized equipment, and an often prohibitively expensive field budget. Environmental DNA (eDNA) is a relatively new method that uses molecular techniques to detect the occupancy of elusive species from water samples and shows the potential to be a more accurate, cost and time-effective method. Pattern recognition software has been successfully used on many different aquatic species to uniquely identify individuals from photographs. The purpose of my thesis was to develop more accurate, non-invasive, and time-effective methods using eDNA and pattern recognition software to replace traditional methods for monitoring populations of at-risk turtles in Ontario. We collected eDNA samples in five wetland complexes at different times of the year, using various techniques to determine the best protocols for detecting the occupancy of Blanding’s turtles in wetlands. Sampling for eDNA will produce more accurate results during the Active season (April-July), and pooling samples from a single wetland can help decrease costs while increasing accuracy. We used 600 photos of shell patterns from 500 different Spotted turtles in the Georgian Bay-Muskoka to uniquely identify Spotted turtles. When photos were pooled across all regions, there was a 90% probability of detecting the correct individual turtle. The use of eDNA and photo identification of turtle plastrons offer cost-effective, non-invasive methods to monitor at-risk freshwater turtle populations and individuals.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".