<scp>GUBIC</scp>: The global urban biological invasions compendium for plants
Bibliographic record
Abstract
Abstract Urban areas are foci for the introduction of non‐native plant species, and they often act as launching sites for invasions into the wider environment. Although interest in biological invasions in urban areas is growing rapidly, and the extent and complexity of problems associated with invasions in these systems have increased, data on the composition and numbers of non‐native plants in urbanized areas remain scattered and idiosyncratic. We assembled data from multiple sources to create the Global Urban Biological Invasions Compendium (GUBIC) for vascular plants representing 553 urban centres from 61 countries across every continent except Antarctica. The GUBIC repository includes 8140 non‐native plant species from 253 families. The number of urban centres in which these non‐native species occurred had a log‐normal distribution, with 65.2% of non‐native species occurring in fewer than 10 urban centres. Practical implications: The dataset has wider applications for urban ecology, invasion biology, macroecology, conservation, urban planning and sustainability. We hope this dataset will stimulate future research in invasion ecology related to the diversity and distributional patterns of non‐native flora across urban centres worldwide. Further, this information should aid the early detection and risk assessment of potential invasive species, inform policy development and assist in setting management priorities.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.034 |
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".