Horizon scanning for invasive non-native species
Bibliographic record
Abstract
Invasive non-native species (INNS) are one of the main drivers of biodiversity loss. Preventing the arrival and establishment of INNS is the most efficient and affordable way of reducing their impacts. Horizon scanning for INNS, which utilises expert-elicitation processes, has been implemented across the globe to predict what species might be next to arrive in a country or territory. This thesis investigates horizon scanning with a focus on the UK Overseas Territories (UKOTs). The 16 UKOTs are geographical and ecologically disparate locations (primarily islands) around the world that hold over 90% of the UK’s biodiversity. As islands, UKOTs are increasingly vulnerable to the impacts of INNS, which are often due to the high levels of endemism and evolution in the absence of generalist predators (like rats and cats). Horizon scanning for INNS has multiple benefits such as increasing awareness of potentially problematic taxa, informing pre- and post-border biosecurity and supporting pathway action planning. Data generated through horizon scanning can also be used to inform early warning and rapid response protocols. This thesis shows that horizon scanning is a reliable method of predicting species arrival, it recommends improvements to the process and outlines how horizon scanning can be used to help deliver global biodiversity targets, such as the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".