Genetic structure of invasive earthworms<i>Dendrobaena octaedra</i>in the boreal forest of Alberta: insights into introduction mechanisms
Bibliographic record
Abstract
Population genetic studies can help to determine whether invasive species are established via single vs. multiple introduction events and also to distinguish among various colonization scenarios. We used this approach to investigate the introduction of Dendrobaena octaedra, a non-native earthworm species, to the boreal forest of northern Alberta. The spread of non-native earthworms in forested systems is not well understood, although bait abandonment and vehicular transport are believed to be important. Mitochondrial DNA sequencing revealed that multiple introductions of this species have occurred in northern Alberta, although individual populations may have been established by either single or multiple invaders introduced on one or more occasions. There was no relationship between genetic distances and either geographical distances or distances along road networks, suggesting that human-mediated jump dispersal is more common than diffusive spread via road networks or via active dispersal. As well, genetic diversity was significantly greater at boat launches than roads, indicating that multiple introductions may be more likely to occur at those locations. Focusing management efforts on areas where multiple introductions are likely to occur may help to reduce invasive species' potential for adaptive evolution and subsequent rapid spread.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".