Active and passive environmental DNA surveillance of aquatic invasive species
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it