Automated Environmental DNA (eDNA) Sampling Using an Optimized Filter Cassette for High Volume Filtration
Bibliographic record
Abstract
An automated water sampling device was developed to collect environmental DNA (eDNA) in aquatic environments with minimal human intervention. Dartmouth Ocean Tech-nologies Inc. (DOT) and NatureMetrics have collaborated to produce an eDNA Sampler equipped with NatureMetrics filters to achieve higher volume filtration. The “DOT-NM eDNA Sampler” demonstrated significantly improved filtering capabilities during benchtop testing when tested in parallel with a standard DOT eDNA Sampler loaded with comparable filter membranes. After benchtop testing, the unit was deployed for a 5-day period in Portchester, UK. A parametric study was performed using different sampling volumes: 1 L, 2 L, and 5 L. Results show that significantly-larger volumes are possible before filter clogging. Metabarcoding analysis of samples revealed a similar number of eukaryotic species detected for each of the three tested volumes. The same analysis, however, revealed a positive correlation between the number of vertebrates detected and the sampled volume. This result highlights the benefit of higher filtration volumes when using environmental DNA sampling to identify vertebrates in an environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".