Resilience of Athabascan subsistence systems to interior Alaska’s changing climateThis article is one of a selection of papers from The Dynamics of Change in Alaska’s Boreal Forests: Resilience and Vulnerability in Response to Climate Warming.
Bibliographic record
Abstract
Subsistence harvesting and wild food production by Athabascan peoples is part of an integrated social–ecological system of interior Alaska. We describe effects of recent trends and future climate change projections on the boreal ecosystem of interior Alaska and relate changes in ecosystem services to Athabascan subsistence. We focus primarily on moose, a keystone terrestrial subsistence resource of villages in that region. Although recent climate change has affected the boreal forest, moose, and Athabascan moose harvesting, a high dependence by village households on moose persists. An historical account of 20th century socioeconomic changes demonstrates that the vulnerability of Athabascan subsistence systems to climatic change has in some respects increased while at the same time has improved aspects of village resilience. In the face of future climate and socioeconomic changes, communities have limited but potentially effective mitigation and adaptation opportunities. The extent to which residents can realize those opportunities depends on the responsiveness of formal and informal institutions to local needs. For example, increases in Alaska’s urban population coupled with climate-induced habitat shifts may increase hunting conflicts in low-moose years. This problem could be mitigated through adaptive co-management strategies that project future moose densities and redirect urban hunters to areas of lower conflict.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".