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Record W1529084179 · doi:10.1139/x10-108

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.

2010· article· en· W1529084179 on OpenAlexvenueno aff
Gary P. Kofinas, F. Stuart Chapin, Shauna BurnSilver, Jennifer I. Schmidt, Nancy Fresco, Knut Kielland, Stephanie Martin, Anna Springsteen, T. Scott Rupp

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersPacific Northwest Research StationU.S. Forest ServiceNational Science Foundation
KeywordsSubsistence agricultureClimate changeGeographyPsychological resilienceVulnerability (computing)EcologyEcosystemPopulationEcosystem servicesEnvironmental resource managementAgricultureEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.385
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations98
Published2010
Admission routes1
Has abstractyes

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