To jump or not to jump: Mule deer and white‐tailed deer fence crossing decisions
Bibliographic record
Abstract
ABSTRACT Modified fencing structures have been recommended with the intention of enhancing ungulate movement. Ungulates such as mule deer ( Odocoileus hemionus ) and white‐tailed deer ( O. virginianus ) typically negotiate fences by jumping over them. We examined 2 fine‐scale fence crossing decisions to determine factors influencing 1) crossing success and 2) the mode of crossing by 2 sympatric deer species. From 2010 to 2016, we used remote cameras along fence lines in 2 study areas—Canadian Forces Base Suffield in southeastern Alberta, Canada, and The Nature Conservancy's Matador Ranch in north‐central Montana, USA—that captured images of deer–fence interactions before and after fence modifications were installed. We used logistic regression to model the probability of deer successfully crossing a fence and mode of crossing (jumping over vs. crawling under) based on fence characteristics and demographic factors. We documented 486 crossing attempts, of which 313 were successful (64.4%), indicating that pasture fences acted as a semipermeable barrier to deer. Of these 313 successful attempts, 152 crawled under the fence (48.6%) as opposed to jumping over it. We documented behavioral differences in mode of crossing between species when successfully crossing a fence. Results indicate that deer are selecting known crossing sites at broad scales as places to negotiate fences, and when assessing finer scale decisions at these sites, white‐tailed deer seemed to acclimate better than mule deer to our imposed changes (switched from crawling under to jumping over the fence). Though sample size was low in terms of use at modified fence sites, we recommend visually inconspicuous modifications (such as clips to increase the bottom wire height as opposed to goat‐bars) when implementing pasture fencing that was friendlier for deer. We also recommend modifications be implemented strategically; placement of modifications may be just as important to consider as the modification type. © 2018 The Wildlife Society.
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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 | 0.002 |
| 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 | 0.000 |
| 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".