Bibliographic record
Abstract
Bats are hosts to a range of pathogens, which include zoonotic pathogens and pathogens of conservation concern. Brock Fenton’s research on bat ecology has always balanced clear communication of potential health risks associated with bats and the need to communicate these risks precisely to avoid unnecessary persecution of bats. Here, we integrate Brock’s work in the field of disease ecology with that of his students and collaborators and consider the potential advantages of studying disease ecology of bats within the Canadian context. The broad distribution of a few common species across the vast landscape of present-day Canada provides an opportunity to untangle the impacts of environmental variation on host–pathogen interactions and disease severity, particularly in the context of climate change. The varying migratory strategies and social structure of the bat species found in Canada could also facilitate informative interspecific studies to better understand how bat health is affected by interactions among rapid environmental changes, physiological traits, and the social behaviour of different species. We propose a series of priority research questions and approaches that could further our understanding of bat health and disease ecology in Canada, inspired by the work of Brock, his colleagues, and students.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".