Mobilizing digitized museum specimen records to highlight important animal pollinators in East Africa
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
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.
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 | 0.001 |
| 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 | 0.000 |
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