Food Storage in Permafrost and Seasonally Frozen Ground in Chukotka and Alaska Communities
Bibliographic record
Abstract
Food cellars, otherwise referred to as ice or meat cellars, (lednik in Russian, k’aetyran in Chukchi, siġļuaq in Iñupiaq, and siqlugaq in Yupik) are a natural form of refrigeration in permafrost or seasonally frozen ground used to preserve, age, and ferment foods harvested for subsistence, including marine mammals, birds, fish, and plants. Indigenous peoples throughout the Arctic have constructed cellars in frozen ground for millennia. This paper focuses on cellars in Russian and American coastal and island communities of the Bering Strait, the region otherwise known as Beringia. This area has a unique, culturally rich, and politically dynamic history. Many traditions associated with cellars are threatened in Chukchi communities in Russia because of the impacts of climate change, relocation, dietary changes, and industrial development. However, even with warmer temperatures, cellars still provide a means to age and ferment food stuffs following traditional methods. In cooperation with local stakeholders, we measured internal temperatures of 18 cellars in 13 communities throughout the Bering Strait region and northern Alaska. Though cellars are widely used in permafrost regions, their structure, usage, and maintenance methods differ and exhibit influences of local climates, traditions, and economic activities. Monitoring internal temperatures and recording structural descriptions of cellars is important in the face of climate change to better understand the variety and resilience of living adaptations in different cold regions.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".