Tackling Invasive Alien Species in Europe: the top 20 issues
Bibliographic record
Abstract
Globally, Invasive Alien Species (IAS) are considered to be one of the major threats to native biodiversity, with the World Conservation Union (IUCN) citing their impacts as 'immense, insidious, and usually irreversible'. It is estimated that 11% of the c. 12,000 alien species in Europe are invasive, causing environmental, economic and social damage; and it is reasonable to expect that the rate of biological invasions into Europe will increase in the coming years. In order to assess the current position regarding IAS in Europe and to determine the issues that were deemed to be most important or critical regarding these damaging species, the international Freshwater Invasives -Networking for Strategy (FINS) conference was convened in Ireland in April 2013. Delegates from throughout Europe and invited speakers from around the world were brought together for the conference. These comprised academics, applied scientists, policy makers, politicians, practitioners and representative stakeholder groups. A horizon scanning and issue prioritization approach was used by in excess of 100 expert delegates in a workshop setting to elucidate the Top 20 IAS issues in Europe. These issues do not focus solely on freshwater habitats and taxa but relate also to marine and terrestrial situations. The Top 20 issues that resulted represent a tool for IAS management and should also be used to support policy makers as they prepare European IAS legislation.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".