An investigation of bat mortality in British Columbia, Canada
Bibliographic record
Abstract
Identifying causes of wildlife mortality can yield an understanding of the factors that impact wildlife health. This is particularly significant for species that are facing population declines because this information can inform conservation and management practices. We evaluated causes of mortality for bats in British Columbia, Canada, submitted to the provincial veterinary laboratory between 2015 and 2020, and assessed whether cause of death varied by species and (or) was associated with bat characteristics (e.g., sex and body condition). Of the 275 bats included in this study, the most frequent cause of death was cat depredation (24%), followed by blunt force trauma (23%). Bats that died by cat depredation tended to be in good body condition compared with those that died from other causes, and male bats were more likely to die from blunt force trauma compared with females. Emaciation was also an important cause of mortality (21%) and 8% of bats died due to rabies, with the greatest rabies prevalence in big brown bats ( Eptesicus fuscus (Palisot de Beauvois, 1796)). Our results demonstrate the potential burden of cat depredation on healthy bats and highlight the need for strategies to decrease cat depredation to support healthy bat populations.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".