Environmental DNA as an ecological monitoring tool for the Canadian Arctic
Bibliographic record
Abstract
Environmental DNA is a method where DNA shed by organisms into the environment is captured and analysed to give insights into various aspects of the ecosystem. Since detection of organisms is based on capturing theoretically as little as a single strand of shed DNA, the method is highly sensitive, cost-effective, and can be applied to any target organism or groups of organisms. These advantages, among others, have led eDNA methods to become a popular tool in environmental monitoring programs. Recent advances in DNA sequencing technologies have lowered the cost of analysis and allowed for many different applications of eDNA to become viable. Despite its widespread use, there are still a limited number of studies that have been conducted in remote regions such as the Canadian Arctic. This project aims to adapt eDNA methods for use in the Canadian Arctic, specifically in the monitoring of several lakes in the vicinity of established and developing mining sites through metabarcoding. Six of the eight target fish species were detected throughout the project, with the addition of one unanticipated species. The established mine site showed little change, while the developing site showed indications of fish movement that were consistent with the change in affected water bodies. Additional work supported the use of eDNA methods in frozen environments where sampling is required through ice. Overall, eDNA sampling was successfully employed in monitoring for fish presence near an active and developing mine in the Canadian Arctic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".