Tear and Crumble: Deterioration Processes in Skins and Hides in Mammal Collections
Bibliographic record
Abstract
Abstract In the mammal collection of the Museum für Naturkunde Berlin (MfN), Germany a serious but inconspicuous deterioration of mammal skins and hides has been detected. The tear strength has been decreasing until the skins are falling apart, risking permanent loss of valuable specimens. At the MfN, about 80% of the 30,000 skins are affected. Although this phenomenon has been known by taxidermists for some time, there are very few publications on the subject. In this study, we surveyed the literature and conducted interviews with collections and leather industry professionals to understand the extent and potential causes of skin deterioration. In addition, analyses of skins in the collections of the MfN and the ZFMK (Bonn, Germany) showed that more than 80% of the tested skins had a very low tear strength. The tear strength appears correlated with the pH value and age of the skin. Our findings suggest that surplus acid from residual fat, preservation methods, or external sources such as air pollution might be a primary source of the degradation. Future steps should include further research on the chemical processes involved in deterioration, treatment options for threatened skins, and development of best practices, protocols for documentation, and development of a publicly available online knowledge base for museum skin preparation, preservation, and storage methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".