Towards a new online species-information system for legumes
Bibliographic record
Abstract
The need for scientists to exchange, share and organise data has resulted in a proliferation of biodiversity research-data portals over recent decades. These cyber-infrastructures have had a major impact on taxonomy and helped the discipline by allowing faster access to bibliographic information, biological and nomenclatural data, and specimen information. Several specialised portals aggregate particular data types for a large number of species, including legumes. Here, we argue that, despite access to such data-aggregation portals, a taxon-focused portal, curated by a community of researchers specialising on a particular taxonomic group and who have the interest, commitment, existing collaborative links, and knowledge necessary to ensure data quality, would be a useful resource in itself and make important contributions to more general data providers. Such an online species-information system focused on Leguminosae (Fabaceae) would serve useful functions in parallel to and different from international data-aggregation portals. We explore best practices for developing a legume-focused portal that would support data sharing, provide a better understanding of what data are available, missing, or erroneous, and, ultimately, facilitate cross-analyses and direct development of novel research. We present a history of legume-focused portals, survey existing data portals to evaluate what is available and which features are of most interest, and discuss how a legume-focused portal might be developed to respond to the needs of the legume-systematics research community and beyond. We propose taking full advantage of existing data sources, informatics tools and protocols to develop a scalable and interactive portal that will be used, contributed to, and fully supported by the legume-systematics community in the easiest manner possible.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".