A validated and optimized environmental<scp>DNA</scp>and<scp>RNA</scp>assay to detect Arctic grayling (<i>Thymallus arcticus</i>)
Bibliographic record
Abstract
Abstract Arctic grayling ( Thymallus arcticus ) is a salmonid fish of significant conservation value. However, conservation efforts are hindered by a lack of fundamental information regarding details such as current population distribution, migratory patterns, and natal habitats. In the current study, we designed, optimized, and field‐ and laboratory‐validated an environmental DNA (eDNA) and environmental RNA (eRNA) assay for Arctic grayling biomonitoring. Using an in silico approach, a robust species‐specific eDNA assay was generated, and filtering and extraction protocols were optimized for maximal eDNA yield. A Preserve, Precipitate, Lyse, Precipitate, and Purify (PPLPP) extraction method generated 70‐fold higher eDNA yields than a column‐based approach. Species‐specificity relative to co‐occurring salmonid fish was validated, and no significant amplification was noted for rainbow trout, brook trout, or mountain whitefish. Shedding rates of eDNA were around eight to nine times higher than those of eRNA, although the two types of nucleic acids decayed at similar rates. Shedding and decay rates were subsequently used to build detection probability models that account for pool size and water exchange rates. These data indicate that eDNA and eRNA are detectable in pools up to 32,500 m 3 in volume and with water flow of less than 0.5 m 3 s −1 when an Arctic grayling is present. This knowledge can be implemented when designing field sampling strategies. Finally, the assay successfully amplified Arctic grayling eDNA from field‐collected samples, with signal strength indicating preferred Arctic grayling habitat or conditions that favored the concentration and retention of eDNA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".