Using <scp>qPCR</scp> of environmental <scp>DNA</scp> (<scp>eDNA</scp>) to estimate the biomass of juvenile Pacific salmon (<i>Oncorhynchus</i> spp.)
Bibliographic record
Abstract
Abstract During the outmigration of Pacific Salmon, the early marine phase is a critical period when high mortality can occur. Traditional sampling and monitoring of juvenile salmon migration can be limited by logistically intensive gear requirements, accessibility, and cost. Improved understanding of the early marine phase, for example, migration duration and habitat use, requires innovative techniques that can improve the spatial and temporal coverage of monitoring. Environmental DNA (eDNA) is genetic fragments present in the environment that can be used as a proxy for organism presence and can be effectively and efficiently collected through water samples. Estimating fish abundance or biomass from eDNA concentration data would provide a valuable fisheries tool but remains challenging to calibrate. To quantify the relationship between eDNA abundance and fish biomass, we used a controlled mesocosm experiment, in which eDNA samples were collected from 15 aquaria (340 L) with varying densities of juvenile Chinook salmon per tank (0, 5, 10, 20, and 30). The concentration of eDNA was obtained by qPCR scaled with fish biomass (ANOVA, p < 0.05). However, we also observed that variability of eDNA concentrations among replicates of the same treatment positively scaled biomass (ANOVA, p < 0.05). Therefore, higher biomasses of fish can yield more challenging data to interpret. This study lays important groundwork for the application of eDNA for monitoring juvenile salmonids yet highlights caveats for the applicability of eDNA as a stand‐alone method to assess biomass in a field setting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".