Detection of Asian carp DNA as part of a Great Lakes basin-wide surveillance program
Bibliographic record
Abstract
Environmental DNA (eDNA) is a sensitive technique for early detection of rare species, including bighead (Hypophthalmichthys nobilis) and silver (Hypophthalmichthys molitrix) carp, which are incipient invaders of the Great Lakes. Since 2009, 2822 samples have been collected from the Great Lakes basin to delimit the extent of Asian carp incursions. Samples collected in the Chicago Area Waterway System and in the western basin of Lake Erie indicate the presence of Asian carp DNA in the Great Lakes. These positive eDNA detections are within 6 and 4 km from where bighead carps were recovered in Lake Calumet, near Lake Michigan (2010), and from Sandusky Bay, Lake Erie (2000), respectively. To implement a Great Lakes surveillance plan for protecting imperiled species and reducing damages from invasive species, federal, state, and provincial agencies will need to cooperatively plan and implement a surveillance program that employs the unique strengths of multiple sampling tools, including eDNA methods.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".