THE EFFECTS OF TANNING AND FIXING PROCESSES ON THE PROPERTIES OF TAXIDERMY SKINS
Bibliographic record
Abstract
The effect of different tanning and fixing processes on the mechanical prop- erties of taxidermy skins was investigated using a screw driven tensile machine. Tanning treatments were potash alum powder, salt and a bath (salt, potash alum, and water) used at the Museum National d'Histoire Naturelle in Paris. Fixing was with formalin or alcohols (methanol, ethanol, and 2-propanol). Stress vs strain plots using results from air-dried skin as control show that air dried skins are least flexible, squirrel skins are significantly stiffer than deer skins but that all the skins tested show elastic properties, except skins fixed in formalin. The MNHN bath produces stronger and more flexible treatments than using potash alum or salt powder on their own. There were no significant differences between ethanol and 2-propanol treated skins at similar concentrations but methanol, ethanol and 2-propanol have increasing flexibility. Increasing ethanol concentration makes the skin more flexible. Even small amounts of formalin increase flexibility and large amounts of formalin are not needed to make a useful skin for taxidermy. Differences of bonding between collagen fibers in the skins account for the differing mechanical properties and suggest ethanol is better than formaldehyde for future preparations. Knowledge of preparation technique is vital when considering future conservation strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".