World checklist of hornworts and liverworts
Bibliographic record
Abstract
A working checklist of accepted taxa worldwide is vital in achieving the goal of developing an online flora of all known plants by 2020 as part of the Global Strategy for Plant Conservation. We here present the first-ever worldwide checklist for liverworts (Marchantiophyta) and hornworts (Anthocerotophyta) that includes 7486 species in 398 genera representing 92 families from the two phyla. The checklist has far reaching implications and applications, including providing a valuable tool for taxonomists and systematists, analyzing phytogeographic and diversity patterns, aiding in the assessment of floristic and taxonomic knowledge, and identifying geographical gaps in our understanding of the global liverwort and hornwort flora. The checklist is derived from a working data set centralizing nomenclature, taxonomy and geography on a global scale. Prior to this effort a lack of centralization has been a major impediment for the study and analysis of species richness, conservation and systematic research at both regional and global scales. The success of this checklist, initiated in 2008, has been underpinned by its community approach involving taxonomic specialists working towards a consensus on taxonomy, nomenclature and distribution.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".