A Miniaturized and Automated eDNA Sampler: Application to a Marine Environment
Bibliographic record
Abstract
Environmental DNA (eDNA) analysis offers a time-and cost-efficient method to perform temporal and spatial observations of aquatic environments, providing a wealth of biodiversity data. Unfortunately, most protocols require manual collection of eDNA samples using highly skilled individuals to capture, filter and store the samples before they can be sent to a lab for analysis. Automated samplers exist to reduce this front-end collection burden, but they tend to be bulky and costly. Here we describe an eDNA sampler designed to be user-friendly and to automate the entire sample collection process. We describe and characterize our eDNA sampler, which offers multi-sample capture, preservation, and self cleaning to reduce cross contamination. DNA sequencing was performed on samples collected using the eDNA sampler as well as a traditional niskin bottle-based protocol at six stations in Bedford Basin, Nova Scotia, Canada. The two approaches showed similar algal and bacterial taxonomic compositions, demonstrating the ability of the eDNA sampler to generate results comparable to those obtained through gold-standard protocols.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".