Introduction pathways and establishment rates of invasive aquatic species in Europe
Bibliographic record
Abstract
Species invasion is one of the leading mechanisms of global environmental change, particularly in freshwater ecosystems. We used the Food and Agriculture Organization's Database of Invasive Aquatic Species to study invasion rates and to analyze invasion pathways within Europe. Of the 123 aquatic species introduced into six contrasting European countries, the average percentage established is 63%, well above the 5%20% suggested by Williamson's "tens" rule. The introduction and establishment transitions are independent of each other, and species that became widely established did so because their introduction was attempted in many countries, not because of a better establishment capability. The most frequently introduced aquatic species in Europe are freshwater fishes. We describe clear introduction pathways of aquatic species into Europe and three types of country are observed: "recipient and donor" (large, midlatitude European countries, such as France, the United Kingdom, and Germany, that give and receive the most introductions), "recipient" (most countries, but particularly southern countries, which give few species but receive many), and "neither recipient nor donor" (only two countries). A path analysis showed that the numbers of species given and received are mediated by the size (area) of the country and population density, but not gross domestic product per capita.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".