Canadian Freshwater Fish Detectionwith Genetics
Bibliographic record
Abstract
Environmental DNA (eDNA) sampling is now routinely utilized to detect aquatic species of conservation concern. Due to the high sensitivity of eDNA assays, DNA sampling can be very effective even when target DNA occurs in low concentrations. However, genetic sequences can be almost identically shared by closely related fish species, constraining the taxonomic resolution of eDNA assays that depend on base pair mismatches to differentiate between taxa. Commonly used metabarcoding assays target sequences approximately 100-150 base pairs in length. To improve taxonomic resolution, we designed two novel universal eDNA metabarcoding (12S) primers that target sequence fragments with significantly greater lengths (250 and 320 base pairs). To validate the amplification of each primer set, we created a list of 21 species from a variety of Canadian freshwater fish taxonomic groups, including, but not limited to, lamprey, sturgeon, and major teleost (bony) fish groups. Overall, the assays successfully amplified all selected species, but Lamprey exhibited limited amplification. To further assess marker performance, we conduct a full-factorial experiment in which DNA from artificial communities is spiked into environmental DNA samples. Mock communities will vary in i) the number of species present (7 or 15 species); and (ii) the distribution of DNA concentrations across species (a few common/mostly rare, even distribution, a few rare/mostly common). Detecting endangered and rare fish species and monitoring their environments is essential for wildlife conservation and management. The metabarcoding assays designed herein will improve the capacity to locate species of conservation concern and facilitate the detection of invasive species.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".