Presence of trailers reduces culvert‐style trap success in American black bears ( <i>Ursus americanus</i> )
Bibliographic record
Abstract
Abstract Live capture is an important tool for wildlife research, conservation, and management as it allows for insight into movement, behavior, demographics, diet, and health of wildlife populations. Live capture is also used for conservation translocations or to remove animals from human‐wildlife conflict situations. Improving trapping efficiency is beneficial for ethical considerations of wildlife handling, human safety, and the best use of limited resources. American black bears ( Ursus americanus ) are often captured using traps. Culvert‐ and box‐style traps are frequently transported using a trailer, and some are affixed to trailers, elevating the trap from the ground. Traps can be set on the ground or on trailers to reduce the set‐up and take‐down times. Although trap elements such as length, height, and visibility can influence trapping efficiency, it is unknown whether trailer presence influences capture success. We assessed the influence of single‐axle trailer presence on capture success of trapping black bears in northeastern Alberta, Canada, during spring 2022 and 2023. We compared capture success between the 2 methods and the influence of trailer presence on age and sex class. Sixty traps were set for a total of 472 trap‐nights resulting in 133 captures of 80 individual bears. Traps set with trailers present required, on average, 1.6 times more trap‐nights per capture (4.9) than those set without trailers (3.1). When only considering new captures, traps with trailers present required 1.9 times more trap‐nights to capture a bear, at 8.5 trap‐nights/capture for traps on trailers and 4.5 trap‐nights/capture when trailers were absent. Trailer presence reduced the success of overall and initial bear captures but did not influence recapture success or the proportion of captured bear age and sex classes. We conclude that wildlife researchers and managers should consider improving trap efficiency of black bears by removing traps from trailers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".