Overwhelming evidence galvanizes a global consensus on the need for action against Invasive Alien Species
Bibliographic record
Abstract
On 4 September 2023, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) released the most comprehensive global synthesis of the current knowledge on the biological invasion process and the impacts of invasive alien species, i.e., the Thematic Assessment Report on Invasive Alien Species and their Control (hereafter IPBES-IAS assessment, IPBES 2023a).This assessment includes data and knowledge from existing databases, peer-reviewed and gray literature, and knowledge from Indigenous Peoples and local communities to gain a global perspective on biological invasions across regions, ecosystems, and taxa (Figs. 1, 2).Here we place the IPBES-IAS assessment in the continuum of invasion science and policy history, describe the assessment process, and discuss the results.While Charles Darwin introduced a remarkable number of concepts relevant to invasion science (Ludsin and Wolfe 2001) and Charles Elton earned
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".