Insufficient collection capacity and facility, bane of plant taxonomic research in Nigeria
Bibliographic record
Abstract
This article explores the pivotal role of herbaria in supporting taxonomic research in Nigeria and highlights the need to improve herbarium infrastructure to enhance plant diversity research in the country. Thirteen herbaria are currently recognized in Nigeria on the Index Herbariorum database and collectively house about 260 000 specimens. The Forest Herbarium Ibadan (FHI) is the largest, containing nearly 50% of these specimens. Based on the occurrence data of Nigerian plants on the Global Biodiversity Information Facility (GBIF), the herbaria, namely FHI, ABUH, LUH, NAUH, and UNICAL contributed only 29.9% compared to international herbaria (70.1%). This disparity underscores the need to strengthen the herbarium collection infrastructure in Nigeria. Taxonomic revisionary studies in Nigeria are very scarce as most of the studies have focused on the morpho-anatomical analysis of plant taxa. The poor taxonomic capacity in the country, which is due to insufficiency of collection capacity and trained taxonomists, has been a bane to the compilation of flora of Nigeria and the inability to document the conservation status of threatened plant species, as more than half (66.0%) of Nigeria’s plants published on GBIF have not been evaluated. There is an urgent need for capacity building for plant collection, curation, and taxonomic review.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".