60 specific eDNA qPCR assays to detect invasive, threatened, and exploited freshwater vertebrates and invertebrates in Eastern Canada
Bibliographic record
Abstract
Abstract Practical applications of environmental DNA (eDNA) are in exponential expansion, especially for the assessment and monitoring of freshwater metazoans. Because eDNA sampling and analysis is noninvasive, it improves the detection of threatened, invasive, and exploited species for which monitoring may be challenging. Species detection efforts using a combination of eDNA and qPCR have been highly successful and, as a result, their use in species monitoring is expanding rapidly. We developed qPCR primers and probes in order to monitor many invasive, threatened, or exploited aquatic species as part of various monitoring eDNA projects in the province of Québec, Canada. Here, we present a total of 60 species‐specific qPCR assays (including PCR protocols, primers, and TaqMan probes sequences) developed for the detection of 45 fishes, six amphibians, five reptiles, two mollusks, and two crustaceans. These comprised nine and 27 species, respectively, listed as invasive and threatened in Eastern Canada. These resources should be of broad usefulness not only for monitoring studies based in Québec but throughout the geographic range of the targeted species in North America.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".