Mobilizing digitized museum specimen records to highlight important animal pollinators in East Africa
Bibliographic record
Abstract
There is an increasing global demand for existing natural history information for use in education, conservation, and policy formulation. Museum specimen collection records, being voluminous, are particularly significant in addressing such demands. This is even more critical in developing countries where daily human life is intimately linked to the environment. We demonstrate how existing museum specimen collection records were mobilized to highlight important animal pollinators in three East African countries. The bulk of the records were obtained from a Specify database of existing zoological collections held at the National Museums of Kenya, and the rest were from such alternative sources as published material, discussions with pollination experts, and online taxonomic portals and other tools. Identified to genus or species level, pollinator-ranking criteria encompassed region-wide distribution, number of plants pollinated, importance index of plants pollinated, and plant dependency on pollination. Overall, insects, especially Apis mellifera, were the most important pollinators in the region, pollinating the largest number of plants of diverse domestic, socioeconomic, and ecological significance. The results underscore potential use of specimen record-based informatics to guide agricultural and economic policy in East Africa.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".