Ways of identifying lichen and plant species by the Nenets reindeer herders in Yamal
Bibliographic record
Abstract
Yamal Nenets herders have historically developed a rich knowledge of lichens and vascular plants, which feature in the diet of their migratory reindeer herds in the tundra zone of northwest Siberia. In the Nenets language there are native names for certain species of lichens and other reindeer forage plants, including graminoids, herbs, shrubs, berries, and mushrooms. During participant fieldwork together with nomadic tundra Nenets herders, we documented names and definitions of reindeer food on herding territories during their long migration routes from the northern forest-tundra transition zone to the northern coastal tundra. Like many other Indigenous peoples of Siberia, Nenets have noticed that the Arctic is changing and some of its recent dynamics are seriously affecting their livelihood. The degradation of some lichen composition and cover on tundra pastures has also contributed to a decrease of herders’ linguistic palette for describing these losses in a concrete manner. Since the Nenets language is on the list of endangered languages of the world, this has an especially negative impact on the language skills and traditional knowledge of the younger generations of Nenets people, who may not know what these lichens look like and why they are important for the Nenets reindeer herding culture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".