Transportation and Large Herbivores
Bibliographic record
Abstract
Large herbivores occur around the world and are often in conflict with roads and vehicles. Large herbivores are plant eaters and are generally hooved (e.g. deer, moose, elephant and buffalo) but also include kangaroos. All play important roles in ecosystem functioning. These are the animals typically involved in most reported wildlife-vehicle collisions (WVC), which have significant costs to human society and wildlife populations. It is important that transportation planners seek to avoid, minimise or mitigate these collisions in order to protect motorists and large herbivore populations. 1 Large herbivores need connectivity across the landscape because restriction of their movements by roads and vehicles will impact wildlife populations, ecosystems and humans. 2 Wildlife-vehicle collisions often involve large herbivores and cause large-scale costs to human societies; documenting WVC and their costs is the first step in preventing them. 3 Mitigating vehicle and road effects on large herbivores requires long-term transportation planning. 4 The type and size of wildlife crossing structures for large herbivores can be partly based on the North American and European experiences. 5 The placement of wildlife crossing structures for large herbivores should be based on animal size, their movement patterns and transportation constraints. 6 Additional mitigation measures include driver warning systems and vegetation management. It is important to consider large herbivores when planning and managing road networks because of their need for frequent and often large-scale movements, their large size which makes WVC dangerous to motorists and their important role in ecosystem function.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 0.001 |
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 teacher head, 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".