Active and passive environmental DNA surveillance of aquatic invasive species
Bibliographic record
Abstract
Environmental DNA (eDNA) is useful for delimiting species ranges in aquatic systems, whereby water samples are screened for the presence of DNA from a single species. However, DNA from many species is collected in every sample, and high-throughput sequencing approaches allow for more passive surveillance where a community of species is identified. In this study, we use active (targeted) and passive molecular surveillance approaches to detect species in the Muskingum River Watershed in Ohio, USA. The presence of bighead carp (Hypophthalmichthys nobilis) eDNA in the Muskingum River Watershed was confirmed with active surveillance using digital droplet polymerase chain reaction (ddPCR). The passive surveillance method detected the presence of eDNA from northern snakehead (Channa argus), which was further confirmed with active ddPCR. Whereas active surveillance may be more sensitive to detecting rare DNA, passive surveillance has the capability of detecting unexpected invasive species. Deploying both active and passive surveillance approaches with the same eDNA samples is beneficial for invasive species management.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".