Testing of candidate non-lethal sampling methods for detection of Renibacterium salmoninarum in juvenile Chinook salmon Oncorhynchus tshawytscha
Bibliographic record
Abstract
Non-lethal pathogen testing can be a useful tool for fish disease research and management. Our research objectives were to determine if (1) fin clips, gill snips, surface mucus scrapings, blood draws, or kidney biopsies could be obtained non-lethally from 3 to 15 g Chinook salmon Oncorhynchus tshawytscha, (2) non-lethal samples could accurately discriminate between fish exposed to the bacterial kidney disease agent Renibacterium salmoninarum and non-exposed fish, and (3) non-lethal samples could serve as proxies for lethal kidney samples to assess infection intensity. Blood draws and kidney biopsies caused ≥5% post-sampling mortality (Objective 1) and may be appropriate only for larger fish, but the other sample types were non-lethal. Sampling was performed over 21 wk following R. salmoninarum immersion challenge of fish from 2 stocks (Objectives 2 and 3), and nested PCR (nPCR) and real-time quantitative PCR (qPCR) results from candidate non-lethal samples were compared with kidney tissue analysis by nPCR, qPCR, bacteriological culture, enzyme-linked immunosorbent assay (ELISA), fluorescent antibody test (FAT) and histopathology/immunohistochemistry. R. salmoninarum was detected by PCR in >50% of fin, gill, and mucus samples from challenged fish. Mucus qPCR was the only non-lethal assay exhibiting both diagnostic sensitivity and specificity estimates>90% for distinguishing between R. salmoninarum-exposed and non-exposed fish and was the best candidate for use as an alternative to lethal kidney sample testing. Mucus qPCR R. salmoninarum quantity estimates reflected changes in kidney bacterial load estimates, as evidenced by significant positive correlations with kidney R. salmoninarum infection intensity scores at all sample times and in both fish stocks, and were not significantly impacted by environmental R. salmoninarum concentrations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".