Global patterns of ranavirus detections
Bibliographic record
Abstract
Ranaviruses are emerging pathogens of poikilothermic vertebrates. In 2015 the Global Ranavirus Reporting System (GRRS) was established as a centralized, open access, online database for reports of the presence (and absence) of ranavirus around the globe. The GRRS has multiple data layers (e.g., location, date, host(s) species, and methods of detection) of use to those studying the epidemiology, ecology, and evolution of this group of viruses. Here we summarize the temporal, spatial, diagnostic, and host-taxonomic patterns of ranavirus reports in the GRRS. The number, distribution, and host diversity of ranavirus reports have increased dramatically since the mid 1990s, presumably in response to increased interest in ranaviruses and the conservation of their hosts, and also the availability of molecular diagnostics. Yet there are clear geographic and taxonomic biases among the reports. We encourage ranavirus researchers to add their studies to the portal because such collation can provide collaborative opportunities and unique insights to our developing knowledge of this pathogen and the emerging infectious disease that it causes.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".