Digitizing the Culicidae collection of Naturalis Biodiversity Center, with a special focus on the former Bonne-Wepster subcollection
Bibliographic record
Abstract
Natural history collections contain a wealth of information on species diversity, distribution and ecology. However, due to historical and practical constraints, this valuable information is not always available to researchers. Our project aimed at unlocking data handwritten in notebooks owned by Johanna Bonne-Wepster, a Culicidae researcher. These handwritten notes refer to specimens labeled with a number only. The notebooks were scanned and entered into a Google spreadsheet. The specimens were provided with a unique identifier, labeled with the information from the notebooks and the data exported to the Global Biodiversity Information Facility. In addition, the type specimens were photographed. Besides Johanna Bonne-Wepster's collection, mosquitoes from the former Rijksmuseum van Natuurlijk Historie collection and the former Zoölogisch Museum Amsterdam Nederland collection were digitized. All specimens are now housed at the Naturalis Biodiversity Center museum in Leiden. This paper describes the efforts to mobilize this data and the problems we encountered.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".