Modeling road mortality hotspots of Eastern Hermann’s tortoise in Romania
Why this work is in the frame
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Bibliographic record
Abstract
Road-associated mortality can lead to local declines of wildlife populations, and management agencies are actively implementing mitigation measures, especially focused on potential road mortality hotspots. In this study we used a spatially-explicit simulation modeling approach to estimate the hotspots of road mortality for the Eastern Hermann’s tortoise ( Testudo hermanni boettgeri ) within its distribution range in Romania. Using a field experiment, we first evaluated velocities while crossing roads. Adult male tortoises moved faster than females (3.98 m/min vs. 2.51 m/min) which led to higher individual probabilities for females being killed on high-traffic roads (0.61 for females vs. 0.44 for males at traffic levels of 7000 vehicles/day). Both males and females had similar road mortality probabilities for traffic levels <1000 and >35 000 vehicles/day. Our spatially explicit model suggests that, within the entire Romanian distributional range, the tortoises have an overall risk of road mortality 1.6%, which may have a negative impact on tortoise populations. Using the Getis-Ord Gi statistic, we identified road mortality hotspots with mortality rates of 5-30%, in areas bisected by high-traffic national and European-level roads. Our research is timely in that many low-traffic roads are predicted to have increased traffic associated with tourism activities, thus increasing the overall risk of mortality. We suggest that mitigation measures such as signage and roadside fences associated with underpasses have the potential to limit road mortality of this threatened species within predicted current mortality hotspots.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it