Importance of sample size for estimating prevalence: a case example of infectious hematopoietic necrosis viral RNA detection in mixed-stock Fraser River sockeye salmon (<i>Oncorhynchus nerka</i>), British Columbia, Canada
Bibliographic record
Abstract
Proper sample size must be considered when designing infectious-agent prevalence studies for mixed-stock fisheries, because bias and uncertainty complicate interpretation of apparent (test)-prevalence estimates. Sample size varies between stocks and are often smaller than expected during wild-salmonid surveys. Our case example of 2010–2016 survey of sockeye salmon (Oncorhynchus nerka) from different stocks of origin in British Columbia, Canada, illustrated the effect of sample size on apparent-prevalence interpretation. Molecular testing (viral RNA RT-qPCR) for infectious hematopoietic necrosis virus (IHNv) revealed large differences in apparent prevalence across wild salmon stocks (much higher from Chilko Lake) and sampling location (fresh water or marine), indicating differences in both stock and host life-stage effects. Ten of the 13 marine non-Chilko stock–years with IHNv-positive results had small sample sizes (<30 samples per stock–year), which, with imperfect diagnostic tests (particularly lower diagnostic sensitivity), could lead to inaccurate apparent-prevalence estimation. When calculating sample size for expected apparent prevalence using different approaches, smaller sample sizes often led to decreased confidence in apparent-prevalence results and decreased power to detect a true difference from a reference value.
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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 | 0.086 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".