Recognizing false positives: synthetic oligonucleotide controls for environmental <scp>DNA</scp> surveillance
Bibliographic record
Abstract
Summary Environmental DNA (eDNA) is increasingly used for surveillance and detection of species of interest in aquatic and soil samples. A significant risk associated with eDNA methods is potential false‐positive results due to laboratory contamination. To minimize and quantify this risk, we designed and validated a set of synthetic oligonucleotides for use as species‐specific positive PCR controls for several high‐profile aquatic invasive species. The controls consist of species‐specific sequences for the species of interest, with the addition of a synthetic insert containing recognition sites for several restriction enzymes. Following PCR, the presence of the synthetic insert can be detected using gel electrophoresis, restriction enzyme digests or DNA sequencing. For quantitative PCR (qPCR), false positives in environmental samples can also be detected using a fluorescent probe designed to detect the synthetic insert. The generation of synthetic controls is a cost‐effective, reproducible method that increases the power and reliability of eDNA testing by eliminating misinterpretation of false‐positive results from laboratory contamination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".