High resilience in the Yamal-Nenets social–ecological system, West Siberian Arctic, Russia
Bibliographic record
Abstract
Tundra ecosystems are vulnerable to hydrocarbon development, in part because small-scale, low-intensity disturbances can affect vegetation, permafrost soils, and wildlife out of proportion to their spatial extent. Scaling up to include human residents, tightly integrated arctic social-ecological systems (SESs) are believed similarly susceptible to industrial impacts and climate change. In contrast to northern Alaska and Canada, most terrestrial and aquatic components of West Siberian oil and gas fields are seasonally exploited by migratory herders, hunters, fishers, and domesticated reindeer (Rangifer tarandus L.). Despite anthropogenic fragmentation and transformation of a large proportion of the environment, recent socioeconomic upheaval, and pronounced climate warming, we find the Yamal-Nenets SES highly resilient according to a few key measures. We detail the remarkable extent to which the system has successfully reorganized in response to recent shocks and evaluate the limits of the system's capacity to respond. Our analytical approach combines quantitative methods with participant observation to understand the overall effects of rapid land use and climate change at the level of the entire Yamal system, detect thresholds crossed using surrogates, and identify potential traps. Institutional constraints and drivers were as important as the documented ecological changes. Particularly crucial to success is the unfettered movement of people and animals in space and time, which allows them to alternately avoid or exploit a wide range of natural and anthropogenic habitats. However, expansion of infrastructure, concomitant terrestrial and freshwater ecosystem degradation, climate change, and a massive influx of workers underway present a looming threat to future resilience.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".