Species Identification of Common Native Arctic Mammals in Inuit Fur Clothing Based on Hair Microscopy
Bibliographic record
Abstract
Correct material identification is considered essential when documenting museum objects. This study examines the morphology of mammal hair and records the geographical use of common species in Inuit fur clothing collected by the National Museum of Denmark (NMD) from c. 1830–1940 in the Bering Strait region, Alaska, Arctic Canada, and Greenland. Through hair microscopy, the purpose is to test whether original identifications are correct to assess the origin of unique Inuit garments. By means of transmitted light microscopy (TLM) of stained, 1 µm thick cross-sectioned hairs and undyed, longitudinally mounted hairs, the research reveals that specific morphological structures are characteristic of the common native reindeer/caribou, musk ox, members of the seal family, domestic dog, wolf, Arctic fox, polar bear, and wolverine. Rarer animals (hare, lynx, otter, etc.) are not part of this study because of limitations in the collection. Hairs from seal species are difficult to distinguish from one another. Hairs from dog and wolf are distinguishable but have relatively similar morphology. Therefore, to confirm identification, supplementary analyses are required. The hair microscopy technique was used on 49 garments in NMD’s collections, and the results were compared to the original macroscopic species identification. The study revealed that the latter method is often erroneous when it comes to dog/wolf and wolverine fur.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".