Sample locations 2022 SARS-CoV-2 testing in little brown bats
Bibliographic record
Abstract
The potential introduction of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, into North American bat populations is of interest to wildlife managers due to recent declines of several species. Populations of little brown bats (Myotis lucifugus) have collapsed due to white-nose syndrome (WNS), a disease caused by the introduction and spread of the fungal pathogen Pseudogymnoascus destructans (Pd). Throughout much of the United States and southern Canada, large colonies of the species routinely established diurnal roosts in anthropogenic structures creating the potential for direct human contact and cross-species disease transmission. Given recent declines and the potential for further disease impacts, we collected oral swabs from eight little brown bat colonies for RT-qPCR analysis to describe the presence and prevalence of SARS-CoV-2. We visited colonies in Maryland (n = 1), New Hampshire (n = 1), New Jersey (n = 2), New York (n = 1), Rhode Island (n = 2), and Virginia (n = 1) during May-August, 2022. We have analyzed samples from 235 individuals and all have tested negative for SARS-CoV-2. Our results indicate that little brown bats are either negative for SARS-CoV-2 or that it persists in undetectable levels in populations of the Mid-Atlantic and Northeast in the summer months. Nonetheless, future work addressing other seasons or using serologic approaches may still be warranted to conclusively determine disease status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 teacher head, 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".