Digitization of the primate collection of the Museum of Nature of the V.N. Karazin Kharkiv National University
Bibliographic record
Abstract
The Museum of Nature of the V.N. Karazin Kharkiv National University (MNKNU) has the largest primate collection in Ukraine, comprising 217 specimens of 58 species, including Homo sapiens. The non-human species represented in the museum comprise 11% of the total number of primates, according to the Mammal Diversity Database v2.2 (further MDD, accessed August 2025), and are distributed across four of the eight biogeographic kingdoms, according to the World Wide Fund for Nature (WWF). The foundation of the MNKNU’s primate collection dates back to the first quarter of the XIXth century, thus, the record for the oldest stuffed animal in the collection - a golden lion tamarin Leontopithecus rosalia (L., 1766), - dates back to 1826. The collection is represented by several types of preservation groups and includes 103 stuffed animals, 87 dry, and 27 wet preparations. In 2009, the museum's Primates catalog was published on paper. Since then, changes to systematics, new additions to the collection, and the urgent need to safeguard information about the collection due to the high risk of destruction by the Russian army have made digitizing the MNKNU Primates collection relevant. It also provides an opportunity to make it available and remotely accessible to a wider range of dedicated specialists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.040 |
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".