A rapid assessment of non-native fish distributions in two English river basins using environmental DNA
Bibliographic record
Abstract
Environmental DNA (eDNA) surveys are increasingly used to inform management decisions for non-native species, for example, by detecting the presence and plotting distributions of species that may be in too low abundance for easy detection by conventional means. A recently-developed nested PCR protocol was used to assess the distributions of three non-native fish species in two river basins of southern England (River Test, Hampshire; River Ouse, Sussex). These river basins were known to contain three non-native fishes, either in the recent past or currently: two invasive small-bodied fish species (topmouth gudgeon Pseudorasbora parva, sunbleak Leucaspius delineatus), as well as a currently non-invasive species predicted to become invasive under future climate conditions, pumpkinseed Lepomis gibbosus. Water samples were collected at locations from headwater streams to estuary. Pumpkinseed and sunbleak were both detected downstream of an angling venue in the Sussex Ouse catchment known to contain those species, with an upstream expansion of sunbleak suggested by the detection of eDNA at a few upstream locations. Neither sunbleak nor topmouth gudgeon was detected in water samples from the River Test catchment, suggesting that neither species has persistent populations in that river catchment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".