Development, Validation, and Implementation of <scp>eDNA</scp>‐Focused <scp>qPCR</scp> Assays to Detect and Distinguish Between Goldfish (<scp><i>Carassius auratus</i></scp>) and Prussian Carp (<scp><i>Carassius gibelio</i></scp>)
Bibliographic record
Abstract
ABSTRACT The ability to accurately detect harmful aquatic invasive species in a species‐specific manner is crucial to monitoring and management efforts. The Canadian province of Alberta currently harbors North America's only invasive Prussian Carp populations, in addition to invasive Goldfish populations. The ability to quickly and accurately distinguish between these phenotypically similar fish, while also determining their presence in various waterbodies, is important in tracking invasions. In this work, we develop a cytochrome B–based assay, as well as an ND2‐based assay to distinguish between these two fish. The 84‐bp‐long CytB assay featured a limit of detection of 5.8 and 4.8 copies/sample for Prussian Carp and Goldfish, respectively, while the 95‐bp‐long ND2 assay featured LODs of 6.3 and 1.6 copies/sample, respectively. We demonstrate that each of these assays fails to amplify these markers in closely related fish species common to Alberta. They also fail to amplify key invasive carp species, apart from the Goldfish ND2 assay, which cross‐reacts with Common Carp. We then implement these assays and find 13 Goldfish and 47 Prussian Carp environmental DNA detection events throughout the Canadian province of Alberta. Finally, we show that assays broadly agree with visual observation data gathered from various reporting mechanisms, highlighting their validity in a monitoring program.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".