Holocene desertification, traditional ecological knowledge, and human resilience in the eastern Gobi Desert, Mongolia
Bibliographic record
Abstract
Dryland regions are particularly challenging for human survival over the course of deep time. This is true for institutionally complex communities as well as small-scale societies that have existed in semi-arid regions throughout the Holocene. This paper examines some of the successful strategies employed by small-scale mobile communities which enhanced their ability to thrive in drylands over the course of thousands of years. Small-scale societies living in drylands must rely on the transmission of Traditional Ecological Knowledge across generations. Some of this knowledge relates to the availability and use of wetlands and other more ephemeral water sources, the exploitation of a diverse range of resources, and the potential for natural storage of food resources as a buffer against regularly occurring drought years in these regions. We compare this understanding with our environmental archeological findings at the Mid-Holocene site of Zaraa Uul in the eastern Gobi Desert of Mongolia. At the site of Zaraa Uul, we show how hunter-gatherer groups returned to a campsite near the edge of a wetland environment over the course of at least two phases during the Mid-Holocene. Here they took advantage of a greater diversity of animal species and plants, including small-grained-grasses and sedges, which could enhance their caloric intake and increase the potential for storable commodities which could be collected as needed from their natural habitat.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".