Evaluation of non-invasive hair snares for North American beavers (<i>Castor canadensis</i>): placement, efficiency, and beaver’s behavioral response
Bibliographic record
Abstract
Although the commercial demand for North American beaver ( Castor canadensis ) hair shaped much of the socio-ecological landscape of North America, use of beaver hair in wildlife research has focused on the Eurasian beaver ( Castor fiber ) and collection methods have largely involved handling animals alive or sampling dead animals. In 2022 and 2023, we tested the utility of barbed-wire hair snares to non-invasively collect hair from beavers around ponds in Northern Minnesota. At 56 different beaver ponds, we deployed 64 hair snares with remote cameras. From these data, we determined the efficiency of hair snares to collect samples, from what side of the body samples are collected, the weight and dirtiness of samples collected, the potential for bycatch, and if snares impede beavers’ ability to travel on land. We collected beaver hair samples from 94% of snares deployed, with snares sampling beaver legs and back most often. Forty-two percent of samples collected had no dirt on them, and the most productive snare collected on average 3.4 mg of clean hair per day. Muskrats were the second most sampled animal, but only made up on average 16% of total samples recorded on video per snare. Snares inhibited beaver travel in 0.1% of videos ( n = 5,627 videos of beavers recorded, n = 6 videos where beaver travel was inhibited). We did not find any predictive variable that influenced the collection of beaver hair ( e.g. , location of snare at pond, presence of wire brushes on snare, number of times beavers touched snares, or location on the beaver’s body that was sampled). Our study provides in depth evidence of passive hair snare methods used to collect North American beaver hair, and serves as a guide to non-invasive hair snaring for multiple objectives such as hormone, genetic, and stable-isotope sample collection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".