Estimating the effects of roads on migration: a barren-ground caribou case study
Bibliographic record
Abstract
A challenge for management of wildlife species is the assessment of the effect of roads on migration. We developed models to estimate the spatial (zone of influence (ZOI)) and temporal (delays in migration) effects of roads, and test whether road closures reduced delays in migration. We analyzed collar (2011–2019) and road survey data from two barren-ground caribou ( Rangifer tarandus groenlandicus (Borowski, 1780)) herds to assess the impact of a 171 km mine road that bisects their migration corridor. We estimated ZOIs of 16–17 km prior to crossing the road during 2018 and 2019, and 3.0 km after crossing. Estimates of mean delay were 4.3 and 2.5 days for 2018 and 2019, which were reduced by 28%–68% (1.2–1.7 days) if roads were closed when caribou were within the ZOI. In 2017–2018, when the road was extended 64 km north, caribou were 12% less likely to deflect north around the road, therefore increasing delays. Road surveys indicated aggregation of caribou prior to crossing the road, with few caribou observed after crossing, a finding supported by collar data. Our methods can estimate the spatial and temporal effects of roads for any wildlife species and assess mitigation strategies in reducing delays in migration.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".