The true cost of partial fencing: Evaluating strategies to reduce reptile road mortality
Bibliographic record
Abstract
ABSTRACT One of the deadliest roads in North America for species at risk fragments a marsh‐lake ecosystem. To reduce road mortality, stakeholders installed >5 km of exclusion fencing along a southwestern Ontario, Canada, causeway in 2008–2009. Between 2012 and 2014, 7 culverts were installed to provide safe crossings. We evaluated the success of these mitigation strategies by 1) comparing results of road surveys conducted 5 years before and 5 years after fencing installation; and 2) monitoring use of culverts by turtles using motion‐activated cameras at culvert openings and stationary antennas placed to detect movements of passive integrated transponder (PIT)‐tagged turtles (68 Blanding's turtles [ Emydoidea blandingii ] and 30 spotted turtles [ Clemmys guttata ]). We also radio‐tracked 30 Blanding's turtles to measure culvert use in relation to home ranges. Turtle and snake abundance was 89% and 53% lower, respectively, in completely fenced road sections than in unfenced sections; abundance was 6% and 10% higher, respectively, between partially fenced and unfenced sections. After mitigation, locations where we found reptiles on the road were associated with fence ends, underscoring the importance of fence integrity and ineffectiveness of partial fencing as a mitigation strategy. We confirmed use of culverts by Blanding's turtles, northern map turtles ( Graptemys geographica ), snapping turtles ( Chelydra serpentina ), and midland painted turtles ( Chrysemys picta ). Through radio‐tracking, we determined that male and female Blanding's turtles home ranges overlapped with different segments of the causeway. We recommend that stakeholders emphasize ensuring fence integrity and continuity, limiting impact of edge effects, and conducting a comprehensive monitoring program. © 2017 The Wildlife Society.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".