Retrofit ecopassages effectively reduce freshwater turtle road mortality in the Lake Simcoe Watershed
Bibliographic record
Abstract
Abstract Although the negative impacts of roads on herpetiles are well documented, broad‐scale implementation of effective mitigation measures to address these impacts remains limited. Here, we evaluated whether a novel, cost‐effective, retrofit ecopassage design can reduce road mortality of herpetiles in the Lake Simcoe Watershed, using a before‐after‐control‐impact study. We also examined whether the ecopassages impacted the movement of turtles across the landscape using wildlife cameras. Our study indicated that the ecopassages significantly reduced turtle road mortality at the treatment sites but were not effective at mitigating road mortality of other herpetiles. Most turtle road‐kill at the ecopassages sites occurred at fence ends, highlighting the need for solutions to address fence‐end effects for herpetiles. There was no evidence that the ecopassages reduced turtles' ability to move between habitats as individuals were observed crossing through the ecopassages. Our results suggest that inexpensive solutions can effectively mitigate road mortality for turtles and taller fencing could improve the design for other species. Ecopassages, such as the ones tested in this study, should be widely implemented in road‐kill hotspots across all regions, especially in habitats of rare or at‐risk species, in order to protect turtles and other wildlife from the increasing threat of roads.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".