Environmental DNA (eDNA) applications in freshwater fisheries management and conservation in Canada: overview of current challenges and opportunities
Bibliographic record
Abstract
Environmental DNA (eDNA) monitoring methods have played a significant role in improving fisheries management decisions. Yet, their impact to date has been rather limited in Canada, where eDNA sampling and analyses are only beginning to be used to inform management and conservation decisions, practices, and policies. Studies investigating hurdles to the incorporation of eDNA evidence into fisheries management decisions generally focus on technical challenges (i.e., risks of false-positive and false-negative detections). We set out to identify challenges that eDNA researchers and conservation practitioners must overcome to fully unlock the benefits of eDNA sampling for fish management in the Canadian context. We discuss aspects of the broad and heterogeneous geography, preponderance of regions located far from densely populated areas, complex political landscape, and cultural diversity of Canada that may complicate the design of reliable eDNA monitoring tools or restrict their use if not adequately addressed. To advocate for the wider use of eDNA sampling, we outline a number of action items that would facilitate the broad adoption of eDNA sampling as a monitoring tool at the Canadian scale.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".