Comparison of Methods of Monitoring Wildlife Crossing‐Structures on Highways
Bibliographic record
Abstract
ABSTRACT Wildlife crossing‐structures (e.g., underpasses and overpasses) are used to mitigate deleterious effects of highways on wildlife populations. Evaluating performance of mitigation measures depends on monitoring structures for wildlife use. We analyzed efficacy of 2 noninvasive methods commonly used to monitor crossing‐structure use by large mammals: tracking and motion‐activated cameras. We monitored 15 crossing‐structures every other day between 29 June and 24 October 2007 along the Trans‐Canada Highway in Alberta, Canada. Our objectives were to determine how species‐specific detection rates are biased by the detection method used, to determine factors contributing to crossing‐event detection, and to evaluate the most cost‐effective approach to monitoring. We detected 3,405 crossing events by tracks and 4,430 crossings events by camera for mammals coyote‐sized and larger. Coyotes ( Canis latrans ) and grizzly bears ( Ursus arctos ) were significantly more likely to be detected by track‐pads, whereas elk ( Cervus elaphus ) and deer ( Odocoileus sp.) were more likely to be detected by cameras. Crossing‐event detection was affected by species, track‐pad length, and number of animals using the crossing structure. At the levels of animal activity observed in our study our economic analysis indicates that cameras are more cost‐effective than track‐pads for study durations >1 year. Understanding the benefits and limitations of camera and track‐pad methods for monitoring large mammal movement at wildlife crossing‐structures will help improve the efficiency of studies designed to evaluate the effectiveness of highway mitigation measures.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".