Effect of Road Traffic on Two Amphibian Species of Differing Vagility
Bibliographic record
Abstract
Abstract: Vehicular traffic can be a major source of mortality for some species. Highly vagile organisms may be at a disadvantage in landscapes with roads because they are more likely to encounter roads and incur traffic mortality. To test this prediction, we assessed the population abundance of two anuran species of differing vagility, the leopard frog ( Rana pipiens , more vagile) and the green frog ( Rana clamitans , less vagile), at 30 breeding ponds. Traffic density, an index of the amount of potential traffic mortality, was measured in concentric circles radiating from the ponds out to 5 km. We conducted multiple linear regressions relating population abundance to traffic density, pond variables, and landscape habitat variables and found that leopard frog population density was negatively affected by traffic density within a radius of 1.5 km. There was no evidence that the presence of vehicular traffic affected green frog populations. These results suggest that traffic mortality can cause population declines and that more vagile species may be more vulnerable to road mortality than less vagile species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".