Blanding's Turtles (<i>Emydoidea blandingii</i>) Avoid Crossing Unpaved and Paved Roads
Bibliographic record
Abstract
Fragmentation of natural landscapes by linear anthropogenic features, such as roads, has several negative consequences, including decreasing connectivity between habitats, inhibiting animal movements, and isolating populations. Roads limit animal movements through behavioral avoidance and mortality during crossing attempts. We investigated the impact of a road network on the movement patterns of Blanding's Turtles (Emydoidea blandingii) in Québec, Canada. We tested the hypothesis that roads act as a barrier to movements. We monitored 52 Blanding's Turtles (22 females, 24 males, and 6 juveniles) via radiotelemetry during their active season from May to August 2010. Road avoidance was quantified for each individual by comparing the number of inferred road crossings with the number of expected road crossings predicted by 1,000 movement path randomizations. Overall, Blanding's Turtles significantly avoided crossing roads. Roads were a significant barrier to movement for 3–6 of the 52 turtles, and an individual's tendency to cross roads was not influenced by its sex or by the road surface (unpaved or paved). Preserving demographic and genetic connectivity of animal populations separated by roads is a major conservation challenge for species at risk such as the Blanding‘s Turtle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".