Application of a quantitative PCR TaqMan™ assay for the detection of Ergasilus labracis in mixed plankton samples from a Newfoundland bay
Bibliographic record
Abstract
Ergasilids are an important group of parasitic copepods that occur globally in some coastal, estuarine, and freshwater habitats, including the south coast of the island of Newfoundland, Canada. Generally, males and developing females are not parasitic and remain in plankton. Adult females, however, become parasitic and seek a host following mating. Few studies have focused on detection and/or quantification of planktonic stages, and of those, all have utilized microscopic techniques. This method is time consuming and dependent on a specific parasitological skill set. In recent years, quantitative PCR (qPCR) techniques have become common in the detection and relative quantification of various invertebrate larval stages within plankton, including many metazoan parasites. In the present study, a qPCR assay using TaqMan™ minor groove binder probe technology, based on the Ergasilus labracis mitochondrial cytochrome c oxidase subunit I sequence, was developed for the first time to detect this parasite in mixed plankton samples taken near active salmonid aquaculture sites in a Northwest Atlantic coastal estuary. Ultimately, the technique can be used for tracking seasonal variability and abundance of planktonic stages of this parasite and thus illustrate patterns of seasonal infestation for both wild and cultured species in this region.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".