Modelling road mortality risks to persistence to a Western Toad ({\it Anaxyrus boreas}) population in British Columbia
Bibliographic record
Abstract
Road mortality may be a significant factor in the global decline of amphibian populations, yet rigorous assessments of its effect on long-term population persistence are lacking. Here, we investigate population persistence through a field study and mathematical model of a western toad ({\textit{Anaxyrus Boreas}} {\RR(Baird and Girard, 1852)}) population within a highway corridor in the Selkirk Mountains of British Columbia. The analysis shows traffic levels strongly correlate with toad mortality, with each additional vehicle causing a 3.1\% $\pm$ 1.3\% ($p=0.020$) increase in toad deaths. Although the current risk of the population becoming threatened or endangered is low, it rises to 50\% if baseline road mortality increases from 10\% to 30\%. Gravid female mortality is higher than the baseline mortality and can increase the probability of endangerment by nearly two-fold at higher baseline mortality levels. We make the case that a small increase in vehicle traffic resulting from future development and recreational pressures could destabilize this apparently healthy toad population. The high sensitivity to traffic levels and rapid transition from healthy to endangered raises concerns for similar populations worldwide. Compensatory structures such as amphibian underpasses (toad tunnels) should be given high priority.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".