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Record W1839128451 · doi:10.1002/grl.50187

Linkages between lake shrinkage/expansion and sublacustrine permafrost distribution determined from remote sensing of interior Alaska, USA

2013· article· en· W1839128451 on OpenAlexaboutno aff
S. M. Jepsen, Clifford I. Voss, Michelle A. Walvoord, Burke J. Minsley, Jennifer Rover

Bibliographic record

VenueGeophysical Research Letters · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
FundersStrategic Environmental Research and Development ProgramU.S. Geological Survey
KeywordsPermafrostAquiferGeologyGroundwaterHydrology (agriculture)Groundwater flowSpatial distributionSurface waterPhysical geographyGeomorphologyEnvironmental scienceRemote sensingOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Linkages between permafrost distribution and lake surface‐area changes in cold regions have not been previously examined over a large scale because of the paucity of subsurface permafrost information. Here, a first large‐scale examination of these linkages is made over a 5150 km 2 area of Yukon Flats, Alaska, USA, by evaluating the relationship between lake surface‐area changes during 1979–2009, derived from Landsat satellite data, and sublacustrine groundwater flow‐path connectivity inferred from a pioneering, airborne geophysical survey of permafrost. The results suggest that the shallow (few tens of meters) thaw state of permafrost has more influence than deeper permafrost conditions on the evolving water budgets of lakes on a multidecadal time scale. In the region studied, these key shallow aquifers have high hydraulic conductivity and great spatial variability in thaw state, making groundwater flow and associated lake level evolution particularly sensitive to climate change owing to the close proximity of these aquifers to the atmosphere.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
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 head0.049
GPT teacher head0.292
Teacher spread0.243 · 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.

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

Citations87
Published2013
Admission routes1
Has abstractyes

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