Towards a new online species-information system for legumes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it