Reducing accidental shrew mortality associated with small-mammal livetrapping I: an inter- and intrastudy analysis
Bibliographic record
Abstract
Shrews (Soricomorpha: Soricidae) are particularly vulnerable to mortality associated with small-mammal livetrapping. We compiled data on mortality rates and protocols from 16 different small-mammal studies and analyzed 16 years of livetrapping data from a single study in Algonquin Provincial Park, Ontario to assess factors affecting shrew mortality. In the comparison across studies, accidental mortality ranged from 10% to 93%. Mortality was lower in studies conducted in locations and at times of year with warmer average temperatures. We found no differences in mortality among bait and trap types across these 16 studies. In our intrastudy analysis from Algonquin Park, Sorex spp. experienced high mortality (81%) regardless of environmental conditions. In contrast, northern short-tailed shrews had low overall mortality (13%), but mortality increased on rainy nights and on colder nights when overnight temperatures dropped below 10°C. Comparisons between 2 trapping protocols within Algonquin Park suggested that both Blarina brevicauda and Sorex sp. experienced higher mortality in Sherman compared with Longworth traps, but these effects were confounded by other differences in methodology between studies. Although we found evidence consistent with some shrew mortality being caused by caloric constraints, experimental studies on bait supplements are necessary to test whether providing high-calorie baits is an efficient method to reduce the widespread shrew mortality that occurs in small-mammal livetrapping studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".