Environmental DNA (eDNA) as a tool to detect Arctic grayling and their habitat preferences in the Northwest Territories, Canada
Bibliographic record
Abstract
Environmental deoxyribonucleic acid (eDNA) assays represent a non-invasive approach for biomonitoring. To assess Arctic grayling ( Thymallus arcticus, Pallas, 1776) populations in the Little Nahanni watershed of Northwest Territories, Canada, an eDNA assay that has been previously validated for use in fish of the Beringia lineage was used in conjunction with traditional survey methods (i.e., electrofishing). Forty-six 100 m reaches of streams were assessed in August 2015. The assay successfully detected Arctic grayling of the Nahanni lineage, with an eDNA signal recorded at each of the 17 sites in which Arctic grayling were observed by traditional fish surveys, but also at 3 of the 29 sites where Arctic grayling were not observed. The presence of eDNA was related to habitat metrics via Random Forest and correlation analyses. Riffles and water temperature were identified as being predictive of Arctic grayling eDNA abundance; however, no significant relationship between eDNA abundance and biomass proxies (fish abundance and fork length metrics) could be established. The high congruence between traditional approaches and eDNA surveys suggests adoption of the latter method will enhance the temporal and spatial acuity of biomonitoring, thereby improving field assessment of Arctic grayling populations and contributing towards more effective conservation management of this 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".