Characterization of Neoparamoeba pemaquidensis strains: PCR-RFLP of the internal transcribed spacer region from the amoeba and endosymbiont
Bibliographic record
Abstract
Neoparamoeba pemaquidensis continues to be an ongoing problem for commercial finfish aquaculture and has also sporadically been associated with mass mortalities of commercially relevant marine invertebrates. Despite the ubiquity and importance of this amphizoic amoeba, our understanding of the biology as it applies to host range, pathogenicity, tissue tropism, and geographic distribution is severely lacking. This may stem from the inability of current diagnostic tests based on morphology, immunology, and molecular biology to differentiate strains at the subspecies level. In the present study, we developed a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method based on the internal transcribed spacer (ITS) region that can accurately differentiate amoeba strains of N. pemaquidensis. The investigation focused on the complications of the amoeba ITS microheterogeneity in the development of a subspecies marker and the use of the endosymbiont, Ichthyobodo necator related organism (IRO), ITS region as an alternative marker. The combination of host amoeba and endosymbiont ITS PCR-RFLP analyses was successfully used to correctly identify and characterize an N. pemaquidensis isolate from an outbreak of amoebic gill disease in Atlantic salmon Salmo salar from the west coast of North America (Washington State, USA).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".