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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".