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Record W2035178181 · doi:10.1029/2011eo180010

A Whole Ecosystem Approach to Studying Climate Change in Interior Alaska

2011· article· en· W2035178181 on OpenAlexaboutno aff
S. G. Riggins, Robert G. Striegl, Michael R. McHale

Bibliographic record

VenueEos · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsSubarctic climatePermafrostBeaverWetlandGeographyWaterfowlArcticWildlifeClimate changeBorealWildlife refugeArctic ecologyEcosystemDrainage basinEcologyArchaeologyHabitat

Abstract

fetched live from OpenAlex

Yukon River Basin Principal Investigators Workshop; Portland, Oregon, 18–20 January 2011 ; High latitudes are known to be particularly susceptible to climate warming, leading to an emphasis of field and modeling research on arctic regions. Subarctic and boreal regions such as the Yukon River Basin (YRB) of interior Alaska and western Canada are less well studied, although they encompass large areas that are vulnerable to changes in forest composition, permafrost distribution, and hydrology. There is an urgent need to understand the resiliency and vulnerability of these complex ecosystems as well as their feedbacks to the global climate system. Consequently, U.S. Geological Survey scientists, with other federal agency, university, and private industry partners, is focusing subarctic interdisciplinary studies on the Beaver Creek Wild and Scenic River watershed ( http://www.blm.gov/pgdata/content/ak/en/prog/nlcs/beavercrk_nwsr.html ) and Yukon Flats National Wildlife Refuge ( http://yukonflats.fws.gov/ ) in the YRB, south and west of Fort Yukon, Alaska. These areas are national treasures of wetlands, lakes, and uplands that support large populations of wildlife and waterfowl and are home to vibrant native Alaskan communities that depend on the area for a subsistence lifestyle.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.130
GPT teacher head0.258
Teacher spread0.128 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2011
Admission routes1
Has abstractyes

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