The use of nuclear ribosomal DNA markers for the identification of bursate nematodes (order Strongylida) and for the diagnosis of infections
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".