Automated eDNA and eRNA Profiling for Biodiversity Monitoring in Marine and Freshwater Ecosystems
Bibliographic record
Abstract
Abstract Automated sampling enables the collection and analysis of eDNA from regions that are limited by site access, sampling times, and operator safety. eDNA sampling devices must be rigorously tested against existing technologies to demonstrate fitness across different operational settings and sample quality. The Dartmouth Ocean Technologies, Inc. (DOT) automated eDNA sampler preserves samples and can be deployed at a range of temperatures and depths. The DOT sampler has previously been tested in marine environments for up to three months, with validation against manual protocols. In this study we tested the DOT sampler in four water bodies in Nova Scotia, Canada, with an expanded set of genetic analyses. We successfully profiled prokaryotes, eukaryotes, and fish using the 16S, 18S, and 12S ribosomal RNA genes respectively, in a brackish pond, a freshwater lake, and two marine harbours. eDNA samples collected by the DOT sampler were statistically concordant with manual Niskin-bottle samples in a range of aqueous habitats. We detected taxonomic groups consistent with the salinity level of each sampled habitat, including invasive species such as smallmouth bass and chain pickerel in the freshwater lake. One marine harbour was sampled at pre-defined time intervals in the days following a significant rainfall event during which site access was limited. We detected ten times as many probable fecal-associated bacteria by proportion at this site relative to the other marine harbour. Onboard preservation of samples in RNAlater allowed the identification of groups with different levels of metabolic activity, and shotgun metagenomic analysis identified key metabolic pathways and a small number of sequences with homology to known antimicrobial-resistance genes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".