Fish skin, a sustainable material used from ancient times to today's fashion
Bibliographic record
Abstract
The use of fish skin is an ancient tradition in Arctic societies along rivers, streams and coasts all over the world. Fish skins were regarded as a useful material for parkas, boots, mittens and hats. Today the interest in making use of fish skin, an undeveloped by-product, is on the rise. By using different tanning techniques from cultures around the globe, fish skin has shown great promise as a material for clothing, as well as other products. There is also a desire to be able to tan these skins with environmentally friendly techniques. Today most animal skins are tanned using chromium and other cheap toxic substances, raising question around health and environmental safety. The knowledge of how to use these traditional tanning methods has been preserved by woman from cultures along the Arctic Circle stretching from the Nordic countries to Canada and Japan. In order to keep this knowledge alive for future generations, Sweden has re-introduced the possibility to receive a Master tanner´s title, increasing the incentive and status for those studying these important subjects. This is a report and narrative review of the field, and insights I have acquired over 3 decades; from student to Master Tanner.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".