Using eDNA to play whack-a-mole with invasive species in green yard waste
Bibliographic record
Abstract
As large cities begin to overrun their landfill capacities, they begin to look for alternative locations to handle the waste stream. Seeing an opportunity to bring in revenue, rural communities offer to handle municipal waste in their landfills. However, many rural communities are also places of agricultural production, which are vulnerable to attacks by invasive insect species, which could be present in green yard waste, the component of municipal waste most likely to contain agriculturally harmful insect species. We used environmental DNA (eDNA) to determine whether green yard waste could be a pathway for invasive insect species to enter and establish in the landfill-receiving agricultural community. We identified several target species that could be in green yard waste coming from Vancouver, BC, Canada, to Central Washington State, USA. We sampled green yard waste from 3 sites every 2 weeks from June to October in 2019 and 2020. DNA was extracted from the nearly 400 samples and subjected to amplification with COI barcoding primers followed by sequencing to identify target insects in the samples. Sequence analyses identified 3 species from the target list: 2 species that are pests of deciduous tree fruits and a generalist root-feeding crop pest. This eDNA technique was useful in identifying potential invasive species in green yard waste and may prove to be an important tool informing policy on the movement of biological material across borders and stemming the spread of invasive species.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".