An Applied Empirical Framework for Invasion Science: Confronting Biological Invasion Through Collaborative Research Aimed at Tool Production
Bibliographic record
Abstract
Abstract Global ecosystem functions, services, and commodities are increasingly threatened by biological invasions. As a result, there is an urgent need to manage invasive species through global collaborative research. We propose an ‘applied empirical framework’ (AEF) to aggressively confront the current global biological invasion crisis. The AEF builds on existing models for invasion science that advocate 1) standardized research designs to reveal key aspects of biological invasion, and 2) collaborative research to facilitate the sharing of resources and information. The AEF further emphasizes the need for 3) the production of research ‘tools’ (e.g., data, methodologies, technical instruments) designed for direct uptake by agencies that manage biological invasion, and 4) a taxonomically targeted approach in which task forces conduct rapid, in-depth research on top-priority invasive species across their entire geographic range. We review collaborative science and the distinctive roles played by different collaborator types. We then provide an example of the AEF in action through the BioSAFE initiative (Biosurveillance of Alien Forest Enemies), a highly collaborative project aimed at developing genomic research tools to facilitate biosurveillance and intervention for forest invasive species. We illustrate the BioSAFE approach through our research on two polyphagous insect species: the wood-borer Anoplophora glabripennis, Motschusky (Coleoptera: Cerambycidae; Asian longhorned beetle) and the defoliator Lymantria dispar, Linnaeus spp. (Lepidoptera: Lymantriidae; gypsy moth). These examples illustrate how the AEF can focus and accelerate our response to the global biological invasion crisis by applying the resource capabilities of collaborative research groups to generate management tools for top-priority invasive species.
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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 | 0.156 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".