Direct and indirect effects of roads on space use by jaguars in Brazil
Bibliographic record
Abstract
Roads pose an imminent threat to wildlife directly through mortality and changes in individual behavior, and also indirectly through modification of the amount and configuration of wildlife habitat. However, few studies have addressed how these mechanisms interact to determine species response to roads. We used structural equation modeling to assess direct and indirect effects (via landscape modification) of roads on space use by jaguars in Brazil, using radio-tracking data available from the literature. We fit path models that directly link jaguars' space use to roads and to land cover, and indirectly link jaguars' space use to roads through the same land cover categories. Our findings show that space use by jaguars was not directly affected by roads, but indirect effects occurred through reductions in natural areas on which jaguars depend, and through urban sprawl. Males´ space use, however, was not negatively influenced by urban areas. Since jaguars seem to ignore roads, mitigation should be directed to road fencing and promoting safe crossings. We argue that planners and managers need to much more seriously take into account the deforestation and the unbridled urban expansion from roads to ensure jaguar conservation in Brazil.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".