Atlas of the butterflies and moths of Nigeria, LepiMap report, 2010–2021
Bibliographic record
Abstract
Interest in citizen science, notably biodiversity mapping, has soared recently in Africa, owing to several converging factors. First, is the growing recognition that biodiversity is threatened, and second, there is a need for collective effort among the public to improve the state of biodiversity, driven by human activities. Amongst the important biodiversity components is Lepidoptera (butterflies and moths) which underpin crucial roles in the ecosystem. Although Lepidoptera has been a major field of study for the past decades, its ecology and distribution have only recently gained important attention among the public, particularly in West Africa. This paper reports on the number of butterflies and moths recorded for Nigeria on the LepiMap database. The database contains 1578 records from January 2010 up to August 2021, from 98 quarter-degree grid cells of the 1306 grid cells in Nigeria (7.5% of grid cells). There are 1219 recorded identified to species level (77%), with 359 awaiting identifications, mostly moths. The number of Lepidopterans recorded was 219 species belonging to 16 families. The most frequently recorded species were Catopsilia florella (28 grid cells, 64 records), Telchinia serena (27 grid cells, 61 records), and Danaus chrysippus alcippus (23 grid cells, 49 records). One of the most important successes of LepiMap during the last three years in Nigeria was the increase in the number of observers and coverage by 600%, which is crucial to gathering lepidopteran mapping data for conservation action. It is recommended that data collection be geared toward areas having no or low coverage, yet refreshing old records are also important for understanding changes in species composition across grid cells.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".