Enumeration potential of environmental <scp>DNA</scp> for Pacific salmon stock assessments
Bibliographic record
Abstract
Abstract The field of environmental DNA (eDNA) has advanced over the past decade, with multiple approaches available for a variety of sampling media and species. While using eDNA for the purpose of simply detecting species is becoming a routine process, the utility of eDNA to estimate species abundance is not well understood. Here, we quantify salmon environmental DNA upstream of a fish counting fence along with river velocity, and together, use these values to determine the correlation between the number of salmon passing by the fish fence daily with daily eDNA rates in water before, during, and after the salmon spawning season for four Pacific salmonids ( Oncorhynchus gorbuscha , O. kisutch , O. tshawytscha , and O. nerka ; pink, coho, chinook, and sockeye, respectively). Throughout the spawning season, approximately 182,000 salmon were counted passing through the fence, of which >98% were pink salmon. Pink salmon exhibited strong correlation between human counts (effect size = 0.65, SE = 0.040) to eDNA rates in the present study and exhibited day‐to‐day variation and a unimodal profile rising and falling with human fish counts. However, the salmon species observed in much lower numbers exhibited a much weaker correlation with eDNA levels higher during the pre‐migratory period than during the migratory period for sockeye, coho, and chinook. Thus, for salmon spawning runs with less than ~1000 adults and daily counts of less than ~100, the juvenile and/or prior seasons eDNA signal appears to be indistinguishable from the adult spawning eDNA signal in our river system. However, for the large pink salmon run, eDNA rates appeared to reflect a local signal of salmon in space and time, essentially tracking these fish within days of passing through the eDNA sampling site.
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".