Global Impacts Dataset of Invasive Alien Species (GIDIAS)
Bibliographic record
Abstract
Invasive alien species are a major driver of global change, impacting biodiversity, ecosystem services, and human livelihoods. To document these impacts, we present the Global Impacts Dataset of Invasive Alien Species (GIDIAS), a dataset on the positive, negative and neutral impacts of invasive alien species on nature, nature's contributions to people, and good quality of life. This dataset arises from the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services' (IPBES) thematic assessment report of this topic. Data were compiled from published sources, including grey literature, reporting a direct observation of an invasive alien species' impact. All impact records contain up to 52 fields of contextual information and attempt to link impacts to the global standard "environmental impact classification for alien taxa" (EICAT) and "socio-economic impact classification for alien taxa" (SEICAT). GIDIAS includes more than 22000 records of impacts caused by 3353 invasive alien species (plants, vertebrates, invertebrates, microorganisms) from all continents and realms (terrestrial, freshwater, marine), extracted from over 6700 sources. We intend GIDIAS to be a global resource for investigating and managing the variety of impacts of invasive alien species across taxa and regions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".