Ecosystem Engineering Among Ancient Pastoralists in Northern Central Asia
Bibliographic record
Abstract
Ecosystem engineering is an innovative concept that recognizes that organisms impact their environment, and that these changes can be detected over time. Thus, additional datasets from the ecological longue durée are necessary, specifically in response to the onset of the Anthropocene and the impacts of humans and their commensal organisms upon ecologies of all scales. For example, the management and herding of domesticated animals are recognized as having dramatic implications for soil stability, vegetation coverage, and even atmospheric composition the world over. Yet, the point at which pastoralism became a recognizable factor in altering earth systems, with large-scale environmental ramifications, is poorly understood. Here, we respond to this by reviewing and presenting data from the archaeological and paleoenvironmental record across northern Central Asia in order to assess broader ecosystem impacts of pastoralism, from time periods when this economic pattern was a relatively novel component of local ecologies and involved limited population densities, through to periods in which it became intensive, coincident with agriculture, and linked to increased sedentism. Probing diverse, published analytical datasets and case studies, we examine pastoral adaptations and environmental impacts, highlighting a region where tensions surrounding resilience and sustainability of pastoralism have peaked in modern times. We draw upon these findings to examine the challenges faced by pastoralists today, and the ways in which archaeological data might inform on management decisions into the future.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".