Invasion trends: An interpretable measure of change is needed to support policy targets
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) calls for a 50% reduction in rates of invasive alien species establishment by 2030. However, estimating changes in rates of introduction and establishment is far from straightforward, particularly on a national scale. Variation in survey effort over time, the absence of data on survey effort, and aspects of the invasion process itself interact in ways that make rate estimates from naive models of invasion trends inaccurate. To support progress towards robust global and national reporting against the GBF invasions target, we illustrate this problem using a combination of simulations, and global and national scale case studies. We provide recommendations and a clear set of steps that are needed for progress. These include routine collection of survey effort data as part of surveillance and monitoring protocols and working closely with researchers to develop meaningful estimates of change in biological invasions. Better awareness of this challenge and investment in developing robust approaches will be required from Parties if progress on Target 6 of the GBF is to be tracked and achieved.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".