The effectiveness of electrified barriers to keep large mammals out of a fenced road corridor and a campground
Bibliographic record
Abstract
For this project the researchers investigated the effectiveness of electrified barriers designed to keep large mammals out of a fenced road corridor (Trans-Canada Highway through Banff and Yoho National Park) and a campground (Lake Louise Campground, Banff National Park). The barriers were designed for large ungulates (e.g. white-tailed deer, mule deer, elk, moose) and large mammal species with paws (e.g. black bear, grizzly bear). The barriers consisted of steel pipes that were partially electrified. None of the white-tailed deer, mule deer, elk, moose, black bears, grizzly bears, red foxes, and coyotes that were observed on the habitat side of the barriers crossed the electrified barriers into the fenced road corridor or the campground. A black bear attempting to exit the fenced road corridor failed to cross to the habitat side of the electrified barrier. Two red foxes and one wolverine did appear to exit the fenced road corridor to the habitat side of the electrified barrier, but these three crossings were all in winter when the voltage was likely compromised because of snow and road salt. In addition, crossings to the habitat side can be considered acceptable as they improve human safety on the main highway and keep the animals from being hit by vehicles. We conclude that, although sample sizes were limited, the electrified barriers (when voltage was adequate and when not filled with snow) were 100% effective in keeping both large ungulates and large species with paws out of a fenced road corridor and a campground.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".