Where to invest in road mitigation? A comparison of multiscale wildlife data to inform roadway prioritization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Roads and associated traffic have significant impacts on wildlife, from direct mortality caused by vehicle collisions to indirect effects when wildlife avoid roads, restricting access to important resources. Road mitigation measures such as constructing wildlife passages over or under the road with directional fencing have proven effective at reducing wildlife vehicle collisions while also enabling wildlife to safely cross the road. Highway mitigation projects are led by transportation agencies with a primary purpose of improving motorist safety. More recently, through the discipline of road ecology, considerations have included safe wildlife passage through transportation corridors. To prioritize road sections for mitigation, data sources include animal vehicle collision data collected by transportation agencies and connectivity models generated by wildlife professionals. We used a third data source, pronghorn observations collected by citizen scientists, and demonstrated its value to prioritize potential wildlife mitigation sites. Our results clearly demonstrate a misalignment of road mitigation sites using animal-vehicle collision data and those of rarer species of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it