Detection of spatiotemporal variation in ranavirus distribution using eDNA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Amphibian population declines have been associated with emerging diseases including ranaviruses, which can cause mass die‐offs across entire amphibian communities. Understanding and mitigating disease spread requires knowledge of spatial and temporal patterns of pathogen distribution, but also how environmental factors influence pathogen occurrence. We applied environmental DNA (eDNA) detection tools to survey spatial and temporal distributions of ranaviruses by sampling 103 waterbodies in southeastern Ontario, Canada and assessed the role of abiotic factors as predictors of pathogen occurrence. Ten waterbodies sampled during June–August (>30 km between sites) revealed that ranavirus was marginally more prevalent ( p = .055) during the latter part of the summer. Ninety‐three sites sampled at a finer scale (<10 km between sites) exhibited seasonal variability in ranavirus detection (site prevalence: 56% May; 66% July). Occupancy modeling revealed that wetland size and elevation influenced ranavirus occurrence while sampling date and water temperature influenced probability of detection. These findings indicate that biotic factors, such as host density and alternative hosts, should be investigated further as likely determinants of ranavirus prevalence across the landscape. Further, these results highlight the sensitivity of eDNA for detecting widespread presence of ranavirus and that abiotic factors may have a limited role in determining its prevalence and infectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 it