TRA-904: ROAD MORTALITY HOT SPOTS OF TURTLES: A THREE-YEAR MINISTRY OF TRANSPORTATION STUDY IN BRANT COUNTY, ONTARIO
Bibliographic record
Abstract
Road mortality has a significant depressive effect on turtle populations in southern Ontario and mitigation measures to reduce turtle road mortality are therefore increasingly being incorporated into road construction projects in the province. This has included the installation of “ecopassages” beneath roadways which enable turtles and other animals to safely move between habitats. These measures are most effective if they are installed at locations with frequent turtle movement and high road mortality rates. We conducted a detailed multi-seasonal survey of turtle road mortality in order to identify mortality “hot spots” along a 4.85 km stretch of highway in Brant County, Ontario. In total, 122 unique observations of dead turtles of two species (midland painted turtle [Chrysemys picta marginata] and snapping turtle [Chelydra serpentina]) were made. Spatial statistical analysis of mortality data using geographic information system (GIS) software was then used to identify mortality hot spots. These hot spots are being used to determine appropriate locations for installing ecopassages and other mitigation measures to reduce turtle mortality along this stretch of highway.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".