An expert curated global legume checklist improves the accuracy of occurrence, biodiversity and taxonomic data
Bibliographic record
Abstract
The Legume Phylogeny Working Group's Taxonomy Working Group was tasked to create a community endorsed global legume checklist that will serve as a primary source of taxa for biodiversity data platforms and legume-related research. The checklist was published in June 2021, recognising 772 genera and 22,360 species. It is disseminated through the new Legume Data Portal as part of the Global Biodiversity Information Facility (GBIF) hosted portal initiative. The process that was followed to publish and disseminate the checklist and its content is described here. The impact of the work by the Taxonomy Working Group are quantified by comparing the published checklist with the GBIF taxonomic backbone. A total of 44,157 names overlapped with the GBIF taxonomic backbone while 30,456 names were added, which enabled more accurate name matching of 61,235 legume occurrences. Continuous improvement to the World Checklist of Vascular Plants (WCVP): Fabaceae checklist will allow the GBIF taxonomic backbone and other checklist managers to converge to a consistent and comprehensive list of legume taxa globally over time.
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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 | 0.037 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".