The use of nuclear ribosomal DNA markers for the identification of bursate nematodes (order Strongylida) and for the diagnosis of infections
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
Many bursate nematodes are of major importance to animal health. Animals are often parasitized by multiple species that differ in their prevalence, relative abundance and/or pathogenicity. Implementation of effective management strategies for these parasites requires reliable methods for their detection in hosts, identification to the species level and measurement of intensity of infection. One major problem is the difficulty of accurately identifying and distinguishing many species of bursate nematode because of the remarkable morphological similarity of their eggs and larvae. The inability to identify, with confidence, individual nematodes (irrespective of their life-cycle stage) to the species level by morphological methods has often led to a search for species-specific genetic markers. Studies over the past 15 years have shown that sequences of the internal transcribed spacers of ribosomal DNA provide useful genetic markers, providing the basis for the development of PCR-based diagnostic tools. Such molecular methods represent powerful tools for studying the systematics, epidemiology and ecology of bursate nematodes and, importantly, for the specific diagnosis of infections in animals and humans, thus contributing to improved control and prevention strategies for these parasites.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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