Using the IUCN Environmental Impact Classification for Alien Taxa to inform decision‐making
Bibliographic record
Abstract
The Environmental Impact Classification for Alien Taxa (EICAT) is an important tool for biological invasion policy and management and has been adopted as an International Union for Conservation of Nature (IUCN) standard to measure the severity of environmental impacts caused by organisms living outside their native ranges. EICAT has already been incorporated into some national and local decision-making procedures, making it a particularly relevant resource for addressing the impact of non-native species. Recently, some of the underlying conceptual principles of EICAT, particularly those related to the use of the precautionary approach, have been challenged. Although still relatively new, guidelines for the application and interpretation of EICAT will be periodically revisited by the IUCN community, based on scientific evidence, to improve the process. Some of the criticisms recently raised are based on subjectively selected assumptions that cannot be generalized and may harm global efforts to manage biological invasions. EICAT adopts a precautionary principle by considering a species' impact history elsewhere because some taxa have traits that can make them inherently more harmful. Furthermore, non-native species are often important drivers of biodiversity loss even in the presence of other pressures. Ignoring the precautionary principle when tackling the impacts of non-native species has led to devastating consequences for human well-being, biodiversity, and ecosystems, as well as poor management outcomes, and thus to significant economic costs. EICAT is a relevant tool because it supports prioritization and management of non-native species and meeting and monitoring progress toward the Kunming-Montreal Global Biodiversity Framework (GBF) Target 6.
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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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".