Residual <scp>eDNA</scp> detection sensitivity assessed by quantitative real‐time <scp>PCR</scp> in a river ecosystem
Bibliographic record
Abstract
Several studies have demonstrated that environmental DNA (eDNA) can be used to detect the presence of aquatic species, days to weeks after the target species has been removed. However, most studies used eDNA analysis in lentic systems (ponds or lakes), or in controlled laboratory experiments. While eDNA degrades rapidly in all aquatic systems, it also undergoes dilution effects and physical destruction in flowing systems, complicating detection in rivers. However, some eDNA (i.e. residual eDNA) can be retained in aquatic systems, even those subject to high flow regimes. Our goal was to determine residual eDNA detection sensitivity using quantitative real-time polymerase chain reaction (qRT-PCR), in a flowing, uncontrolled river after the eDNA source was removed from the system; we repeated the experiment over 2 years. Residual eDNA had the strongest signal strength at the original source site and was detectable there up to 11.5 h after eDNA source removal. Residual eDNA signal strength decreased as sampling distance downstream from the eDNA source site increased, and was no longer detectable at the source site 48 h after the eDNA source water was exhausted in both experiments. This experiment shows that residual eDNA sampled in surface water can be mapped quantitatively using qRT-PCR, which allows a more accurate spatial identification of the target species location in lotic systems, and relative residual eDNA signal strength may allow the determination of the timing of the presence of target species.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".