Prioritizing human safety and multispecies connectivity across a regional road network
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
Abstract The intersection of wildlife and people on roads raises two critical issues: the barrier and mortality effects of roads on wildlife and risks to people from animal‐vehicle collisions (AVCs). Road mitigation decisions are typically made at the discretion of transportation departments that are mandated to primarily address motorist safety. Therefore, prioritization of road sections for mitigation currently focuses on identification of spatial clusters of AVCs. We sought to understand if AVC clusters align with multispecies connectivity across roads to accurately identify multipurpose mitigation hotspots. We developed a decision‐support tool based on weighted priorities for mitigation planning across 7,900 km of roads over an 84,000‐km 2 area of southern Alberta, Canada. To assess alignment, we built functional connectivity models for four focal species (prairie rattlesnake, grizzly bear, mule deer, and pronghorn) and a species‐neutral structural connectivity model. We integrated AVC risk and wildlife connectivity indices into Mitigation Priority Indices that varied the weighting of individual indices. Our results demonstrated poor spatial alignment between road sections of high motorist safety risk and those of high value for wildlife connectivity. Transportation planning would benefit from integrating motorist safety risk and wildlife management needs to prioritize mitigation neighborhoods along roadways.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| 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