b3alien: A Python package to assess the introduction rate of alien species in a FAIR and reproducible way
Bibliographic record
Abstract
Abstract The Kunming–Montreal Global Biodiversity Framework outlines an ambitious pathway to achieve harmony with nature by 2050, with 23 targets for 2030. Among them, Target 6 seeks to reduce invasive alien species (IAS) introductions by 50% and minimize their impacts. Achieving and monitoring progress towards this target is highly challenging, as observed first records rarely reflect true introduction rates due to detection lags influenced by survey effort, detectability, and taxonomic expertise. To address this, statistical methods, such as the approach proposed by Solow and Costello, account for detection delays and provide a more reliable basis for estimating IAS establishment rates. This forms the basis of “Headline Indicator 6.1” within a wider suite of component and complementary indicators that together describe invasion dynamics and impacts. Reliable monitoring requires transparent, reproducible tools that can integrate diverse data sources. Here, we present b3alien, a software package developed to facilitate calculation of Target 6 indicators. Built on the biodiversity data cube framework from the Biodiversity Building Blocks for Policy (B-Cubed) project, b3alien leverages the Global Biodiversity Information Facility (GBIF) infrastructure, including its Taxonomic Backbone and the Global Register of Introduced and Invasive Species (GRIIS). By integrating GBIF occurrence records with checklists and complementary datasets, the tool enables robust estimation of IAS establishment rates while supporting additional invasion-related indicators. Outputs are technically rigorous yet accessible, ensuring usability for policymakers and stakeholders. The basic workflow is using a GBIF-based occurrence cube, which can be extended by incorporating citizen science contributions, private datasets, and customized checklists, thereby ensuring flexibility and adaptability across contexts. By aligning with FAIR data principles, b3alien ensures indicators are findable, accessible, interoperable, and reusable. Importantly, the approach empowers countries to build their own indicators on open, community-driven infrastructures, lowering technical and financial barriers, fostering bottom-up ownership, and ensuring scientific and policy credibility. In summary, b3alien demonstrates how open infrastructures, standardized data, and reproducible workflows can make IAS monitoring both accessible and scientifically robust. By bridging biodiversity data with actionable policy insights, it provides a practical and equitable pathway to support the ambitions of the Global Biodiversity Framework’s 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.036 |
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".