Using <scp>qPCR</scp> of environmental <scp>DNA</scp> (<scp>eDNA</scp>) to estimate the biomass of juvenile Pacific salmon (<i>Oncorhynchus</i> spp.)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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 it