Laboratory and field validation of a simple method for detecting four species of non‐native freshwater fish using <scp>eDNA</scp>
Bibliographic record
Abstract
This paper presents the first phase in the development and validation of a simple and reliable environmental (e)DNA method using conventional PCR to detect four species of non-native freshwater fish: pumpkinseed Lepomis gibbosus, sunbleak Leucaspius delineatus, fathead minnow Pimephales promelas and topmouth gudgeon Pseudorasbora parva. The efficacy of the approach was demonstrated in indoor tank (44 l) trials in which all four species were detected within 24 h. Validation was through two field trials, in which L. gibbosus was detected 6-12 h after its introduction into outdoor experimental ponds and P. parva was successfully detected in disused fish rearing ponds where the species was known to exist. Thus, the filtration of small (30 ml) volumes of pond water was sufficient to capture fish eDNA and the approach emphasised the importance of taking multiple water samples of sufficient spatial coverage for detecting species of random or patchy distribution.
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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 | 0.000 | 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 | 0.000 |
| 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".