Introducing a Novel Initiative for Mitigating the Impacts of Road Mortality on Turtles in Brampton, Ontario Using 3D Printed Models: A One Health Perspective
Bibliographic record
Abstract
With 7 out of 8 turtle species in Ontario classified as at-risk, vehicular-reptile collisions on roads pose a serious threat to turtle populations provincially. These species, however, play crucial roles in maintaining the health of humans, non-human animals, and the environment. By providing ecosystem services like nutrient cycling, population control, and pollination, turtles are essential in semi-aquatic ecosystems like wetlands. However, the need for road infrastructure to support human populations, failures in exclusion fencing and eco-passages, and the varied perspectives of numerous stakeholders make this issue particularly difficult to solve. Despite these challenges, a novel initiative taking place at Heart Lake Conservation Area in Brampton, Ontario may provide an alternative solution. Using 3D printed models, Toronto and Region Conservation Authority (TRCA), in partnership with the Brampton Library and Heart Lake Turtle Troopers, is attempting to reduce mortality along roads by leveraging knowledge about the nesting preferences of female turtles. From a One Health perspective, this novel initiative engages multiple stakeholders to create an interdisciplinary solution combining technology, art, and science to protect turtle populations in the 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".