Digitization of the primate collection at 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 59 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 of 54 determined species, 87 dry, and 27 wet preparations. In the present work, we report the result of the revision of the primates collection, including taxonomical redetermination, as well as performed digitization of the part of the collection (represented by stuffed animals) accompanied by the original historical inventory data (index cards catalogue, inventory books, and printed catalogues).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".