Do pronghorn (Antilocapra americana) perceive roads as a predation risk?
Bibliographic record
Abstract
The risk–disturbance hypothesis proposes that organisms respond to generalized threat stimuli; therefore, human disturbances that elicit these behaviours will cause individuals to behave similarly to avoid a natural predator. Studies have shown that pronghorn antelope, Antilocapra americana (Ord, 1815), are influenced by human disturbances. We examined several intensities of human activity and distance from disturbances as indicators of risk perception in pronghorns. We investigated whether pronghorns exhibited risk-avoidance behaviour towards road traffic consistent with the risk–disturbance hypothesis by comparing vigilance and foraging behaviour observations across increasing traffic levels and proximity to roads. Pronghorns showed higher vigilance and lower foraging times along high traffic roads during the spring season compared with lower traffic levels, suggesting that risk perception is related to traffic level. Moreover, individuals within close proximity to roads regardless of traffic level exhibited higher vigilance levels, indicating that there is an overall risk perceived towards roads. Our results also suggest that individuals in herds with young are more risk averse than other social groupings and individuals in larger groups perceive less risk. We suggest that consequences of risk-avoidance behaviour should be reflected in land-use plans that address road densities and traffic levels to better manage wildlife.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".