Participatory Design Wiki:Call for Participation
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
Viral gastroenteritis is a common clinical problem in dogs and group A rotavirus (RVA) is one of the agents involved in this etiology. It mainly affects dogs in the first 6 months of life, and these animals are considered an important reservoir and potential transmitters of the virus to other susceptible hosts, such as humans. Among the different types of RVA, G3 is the most detected in dogs, and this genotype is also involved in infections in other animals, including humans. Thus, the present study aims to investigate the presence of RVA in samples of dogs from a public kennel. A total of 64 fecal samples from dogs with diarrhea were analyzed, collected from April 2019 to March 2020, from the kennel of the Zoonosis Control Center, located in Belém, a city in the North of Brazil. The extracted genetic material was subjected to reverse transcription followed by real-time PCR (RT-qPCR); the positives were tested by RT-PCR with a specific primer for the RVA VP7 gene, after nucleotide sequencing and phylogenetic analysis. One sample was subjected to high-performance sequencing. A positivity of 7.8% (5/64) was observed for RVA, all characterized as G3, grouping in the G3-III lineage, with greater similarity to human samples. Different regions of the RVA genome fragments were found. These results emphasize the need for animal health surveillance to better understand the global strain dispersion of RVA and elucidate possible interspecies transmission events, monitoring the genetic diversity of this pathogen.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it