Warning signs mitigate deer–vehicle collisions in an Urban area
Bibliographic record
Abstract
Abstract Increasing collisions with deer ( Odocoileus spp.) and other large animals, and the rise in associated public safety risks and economic costs, have made mitigation strategies a priority for both transportation and wildlife managers. Deer‐crossing warning signage is one of the oldest forms of mitigating deer–vehicle collisions (DVCs), but despite their low cost and logistical simplicity, the effectiveness of standard‐sized permanent warning signage at reducing DVCs has not previously been adequately determined. We used historical DVC data, based on deer carcass retrieval, to identify and target areas and periods of high collision frequency. We installed warning signs at these high collision frequency locations and then compared DVCs to un‐signed control locations. The total number of DVCs at signed hotspots was significantly different in the year after the signs were installed, compared to the 3 prior years ( F 13 = 4.99, P = 0.004). Although the single year of posttreatment data means the long‐term efficacy of warning signage remains unknown, we showed that in the first year after installation, deer‐crossing signs targeting high collision locations can be effective at reducing DVCs. © 2011 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".