Reliability and reproducibility in targeted eDNA detection: the implications of species distributions and genetic diversity
Bibliographic record
Abstract
Environmental DNA (eDNA) sampling is an established tool for biodiversity studies. The design of assays to detect species’ DNA in samples underpins this tool. Although much progress has been made to develop validation frameworks for assay performance, an in silico validation framework is sorely missing. A conceptual framework was developed to incorporate the elements of assay design and likely outcomes of assay performance through a step-by-step decision tree evaluating in silico sensitivity and specificity. Using an in silico PCR tool, the sensitivity and specificity of published assays were evaluated to determine the potential ability of an assay to amplify and detect known target and non-target species sequences and place them within the conceptual framework. Results showed that most published assays tested exhibited the potential for Type I and/or Type II errors, with very few assays showing unique species specificity. Results also demonstrate that in silico validation can predict potential problematic non-target species co-amplification. The developed conceptual framework demonstrates the ability to provide a robust and reproducible in silico validation step highlighting key components of the design stage that need to be published to improve transparency, repeatability, reproducibility, and confidence in eDNA sampling.
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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 | 0.128 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".