Relative efficiency of two models of snap traps for sampling boreal small mammals
Bibliographic record
Abstract
Abstract Snap traps have long been a cost‐effective means of monitoring small mammal (<60 g) diversity and abundance, particularly at larger spatial or temporal scales. Yet, studies on the relative efficiency of snap trap models are surprisingly rare in the literature. We assessed the relative efficacy of Victor Mouse and Woodstream Museum Special snap traps for sampling boreal small mammals. Paired traps were set for 2–3 nights at 40–50 trapping stations at each of 110 sites, for a total sampling effort of 28,910 trap nights. We captured 1,013 small mammals representing 13 species. Overall, Museum Special traps caught almost twice as many small mammals as Victor traps. There was no difference in the sex or age‐class of the overall capture in the 2 trap models. However, Museum Special traps were triggered without capturing a small mammal 31% more often than Victor traps. Results for the six most frequently captured species ( Myodes rutilus , Microtus pennsylvanicus , Microtus oeconomus , Microtus xanthognathus , Peromyscus maniculatus , and Sorex cinereus ) mirrored those for overall captures. Moreover, the percent of the total capture in Museum Special traps ranged between 57–80% for each of the above species, indicating species‐specific responses to trap type. Our data further demonstrate the superior ability of Museum Special traps to capture boreal small mammals compared to Victor traps, which is likely attributed to a more sensitive trigging mechanism. Implications of our results suggest caution when mixing trap models in monitoring programs, or when interpreting results obtained with different trap models. We encourage similar comparisons in different biomes with different small mammal assemblages as trap performance is likely species specific.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".