Consumption of Bats (Myotis spp.) by Raccoons (Procyon lotor) During an Outbreak of White-Nose Syndrome in New Brunswick, Canada: Implications for Estimates of Bat Mortality
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) McAlpine, Donald F., Karen J. Vanderwolf, Graham J. Forbes, and David Malloch. 2011. Consumption of bats (Myotis spp.) by Raccoons (Procyon lotor) during an outbreak of white-nose syndrome in New Brunswick, Canada: implications for estimates of bat mortality. Canadian Field-Naturalist 125(3): 257–260. Across their range, Raccoons (Procyon lotor) will opportunistically exploit bats (Chiroptera) roosting in caves as a source of food. During a significant mortality event associated with white-nose syndrome (WNS) at a cave in eastern Canada, we estimate that Raccoons consumed 3169–3827 dead and dying Little Brown Bats (Myotis lucifugus) and Northern Long-eared Bats (M. septentrionalis) infected with white-nose syndrome, equivalent to 62.0–74.9% of the total bat mortality at this site. However, the generally small dispersal distances of Raccoons and their reduced activity during the period when bats are hibernating suggest that Raccoons are likely not a significant vector for moving the fungus associated with white-nose syndrome, Geomyces destructans, between most caves at this latitude. Nevertheless, since we show that significant numbers of bats can be consumed in hibernacula through opportunistic feeding by Raccoons, estimates of in-cave mortality due to white-nose syndrome should incorporate any evidence of consumption of bats by Raccoons and other predators.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".