Detecting the brook floater, a freshwater mussel species at risk, using environmental DNA
Bibliographic record
Abstract
Abstract The use of environmental DNA (eDNA) is a promising approach for the detection of aquatic species, including species at risk. One freshwater mussel species of interest in Atlantic Canada, the brook floater (Alasmidonta varicosa), is listed as being of Special Concern under theSpecies at Risk Actin Canada and as Vulnerable on the International Union for Conservation of Nature Red List. Further scientific data regarding species distribution and critical habitat is needed for the protection and conservation of this species. The aim of this study was to design, optimize, and apply a species‐specific quantitative polymerase chain reaction assay for the detection of brook floater from eDNA samples, and to assess temporal variability in brook floater eDNA quantities. Through an eDNA survey performed in New Brunswick rivers in 2017 and 2018, brook floater DNA was found at a total of 16 out of 56 sites sampled. The amount of brook floater DNA detected at all 16 sites was always below the theoretical limit of detection of the assay, and, as such, results were classified as either ‘inconclusive’ or ‘suspected’. The co‐detection of eastern pearlshell (Margaritifera margaritifera), a more abundant freshwater mussel species in Atlantic Canada, was successfully used as a natural positive control. Temporal variability in the amount of eDNA found in the water was also assessed at a site with a known brook floater population and minimal variability in eDNA quantities was observed from May to September. These results provide researchers and managers with a new tool for the detection of the brook floater in support of conservation and monitoring efforts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".