Prioritising environmental invasive weeds of European concern for classical biological control: A reanalysis
Bibliographic record
Abstract
Abstract Invasive alien plant species (IAPs) are causing significant negative impacts on agricultural production, threatened native species and ecosystems, the services they provide and public health thereby affecting European biodiversity and its economy. IAPs invade all types of natural and managed habitat and their impacts, through increased numbers and area invaded, are growing exponentially. Current control options in Europe are largely limited to manual and chemical control, which is high cost, short‐term in effectiveness and with regards to chemical control declining in public acceptability. Globally, classical biological control (CBC) is widely and successfully used to manage many IAPs. CBC aims to redress the ecological imbalance caused by the IAPs, generally being released without their natural enemies. The steps are to select, risk assess potential specific natural enemy biocontrol agents of the IAP (from the IAP's native range) and follow regulatory approval prior to releasing them to ecologically suppress their abundance. CBC is not widely used in Europe. Only five active programmes exist. In this paper, we apply an existing framework to develop a ranked list of environmental IAPs named in the EU Regulation on Invasive Species for biocontrol. We used a scoring system based on existing knowledge on the IAPs impacts, the amount of effort needed to deliver a CBC programme targeting them and the feasibility and likelihood of success of such programmes. We identify 16 IAPs in Europe for which CBC has relatively high potential and discuss existing knowledge that can underpin any future investments in such activities against each of these IAPs. The top three species being Pontederia crassipes , Pistia startiotes and Acacia saligna . This research should support decision‐making on the instigation of future CBC programmes against environmental IAPs in Europe. We set this analysis in the context of other operational and regulatory constraints on developing CBC programmes against environmental IAPs in Europe.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".