Determining the ability of avian species to act as vectors for ranavirus transmission
Bibliographic record
Abstract
Ranaviruses are a group of large double stranded DNA viruses that cause massive die-offs in natural populations of ectothermic vertebrates and pose a threat to lentic ecosystem health and food web dynamics. Although Ranaviruses are extremely prevalent worldwide, knowledge on their distribution patterns and ecological dynamics are limited. Previous studies have determined that certain bird species have been able to carry the Influenza A Virus and Chytrid (Batrachochytrium dendrobatidis) on their feathers and present the ability to distribute it to other susceptible communities. Canada Geese (Branta. Canadensis) often use wetlands where amphibian communities are present and are able to travel long distances. The goal of this study is to determine the capability of wild populations of Branta canadensis to act as a vector for the transmission of ranavirus. Throughout Vermont, wild individuals of B. canadensis were swabbed and tested for ranavirus using quantitative PCR (qPCR). We anticipate ranavirus to persist within the feathers. The findings of this study will provide further explanation of the quick spread and global distribution of ranavirus. Transmission is key to understanding pathogen fitness and the impact pathogens have on host populations. By determining the role birds play in transmitting ranavirus we can better understand the distribution patterns and aid in conservation strategies.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".