Injury Rates of Freshwater Turtles on a Recreational Waterway in Ontario, Canada
Bibliographic record
Abstract
Highly aquatic freshwater turtle species are at risk of vehicular encounters during terrestrial nesting forays; however, injuries and mortality incurred during boat collisions may present considerable threats as well. Additionally, effects of other injuries from predation or disease may augment the likelihood of population decline. We report injury rates from captures of two turtle species-at-risk, Northern Map Turtles (Graptemys geographica) and Stinkpots (Sternotherus odoratus), along the Trent–Severn Waterway in Ontario, Canada. We examined whether habitat fragmentation attributable to locks and dams would result in higher rates of boat propeller and predation injuries because of the higher human impact in fragmented areas. Fragmented areas of the waterway had similar injury rates to continuous areas; however, more female Map Turtles (28.6% of captured females) had injuries consistent with boat propeller strikes than males (12.8%). Map Turtles in general had higher rates of injury (48.5%) than Stinkpot Turtles (20.0%), although actual rates of boat or predator encounters may be confounded by the lowered probability of survival for a smaller-bodied turtle (e.g., Stinkpot, male Map Turtles). All species encountered on the waterway, including incidental captures of Blanding's Turtles (Emydoidea blandingii) and Snapping Turtles (Chelydra serpentina), showed some evidence of boat propeller strikes, suggesting that conservation strategies for aquatic turtle assemblages should consider restricting boat access, speed limits, or both, in areas of high turtle densities.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".