Look both ways: factors affecting roadkill probability in Blue-black Grassquits (<i>Volatinia jacarina</i>)
Bibliographic record
Abstract
The contribution of roads to human economic and social development contrasts with its impact on the environment and wildlife. One of the most significant impacts of roads is wildlife–vehicle collisions. Millions of individuals from numerous species are killed annually around the world. Here we investigated the spatial and temporal dynamics of road killing on a small neotropical bird, the Blue-black Grassquit (Volatinia jacarina (Linnaeus, 1766)). We used a data set of roadkill records collected between 2010 and 2015 to test the hypotheses that roadkills are concentrated during the breeding period of the species and that road features and weather conditions affect the likelihood of animal–vehicle collisions. We observed that the number of fatalities was temporally and spatially clustered. Roadkills were more frequent in warmer, rainy days with lower wind speed. Fatalities were more commonly associated with two-lane roads compared with dirt and four-lane roads. Given that Blue-black Grassquits are attracted to human-modified habitats, especially to artificial grasslands composed of exotic grasses usually found along the margins of roads, mitigation measures should focus on the management and control of grass populations. Roadside mowing may reduce areas where Blue-black Grassquits can establish territories, and consequently, reduce the activity of the species near roads.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".