Revisiting the hypothesis of sex-biased turtle road mortality
Bibliographic record
Abstract
Road mortality poses a major threat to turtle populations. Several studies have suggested that the terrestrial movements associated with nesting increase this risk for females. The Ontario Turtle Conservation Centre (OTCC) is home to the Kawartha Turtle Trauma Centre, which admits 900 or more turtles a year, with road injuries the primary cause of admission. We tested the hypothesis that road mortality in turtles is female-biased using data from injured Midland Painted Turtles (Chrysemys picta marginata), Snapping Turtles (Chelydra serpentina), Blanding’s Turtles (Emydoidea blandingii), and Northern Map Turtles (Graptemys geographica) collected over about 126 000 km2 and admitted to OTCC’s hospital from January 2013 to October 2017. There was no difference in the number of male and female admissions of Midland Painted, Blanding’s, or Snapping Turtles (P > 0.05); however, more female Northern Map Turtles than males were admitted (P < 0.001). Admission of female turtles peaked in June during the nesting season, but male admissions were more evenly distributed throughout the season. Our admissions data provide a temporally unbiased and geographically broad snapshot of turtle–vehicle interactions that can directly inform conservation and management policies. Although our data are not equivalent to mortality rates, these results demonstrate that vehicle strikes can have a substantial impact on both female and male turtles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".