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Record W2727303116 · doi:10.1002/2017gl073834

Glacierized headwater streams as aquifer recharge corridors, subarctic Alaska

2017· article· en· W2727303116 on OpenAlexaboutno aff
Anna Liljedahl, Anne Gädeke, S. O’Neel, Tiffany Gatesman, Thomas A. Douglas

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersEngineer Research and Development CenterStrategic Environmental Research and Development ProgramU.S. Geological SurveyNational Institutes for Water ResourcesAlaska Department of Transportation and Public FacilitiesNational Science FoundationU.S. Department of TransportationU.S. Department of Defense
KeywordsGroundwater rechargeAquiferHydrology (agriculture)PermafrostStreamflowGlacierMeltwaterGeologySurface runoffSTREAMSBaseflowPerennial streamSubarctic climateSnowmeltDrainage basinEnvironmental scienceGroundwaterSnowGeomorphologyOceanographyGeographyEcology

Abstract

fetched live from OpenAlex

Abstract Arctic river discharge has increased in recent decades although sources and mechanisms remain debated. Abundant literature documents permafrost thaw and mountain glacier shrinkage over the past decades. Here we link glacier runoff to aquifer recharge via a losing headwater stream in subarctic Interior Alaska. Field measurements in Jarvis Creek (634 km2), a subbasin of the Tanana and Yukon Rivers, show glacier meltwater runoff as a large component (15–28%) of total annual streamflow despite low glacier cover (3%). About half of annual headwater streamflow is lost to the aquifer (38 to 56%). The estimated long‐term change in glacier‐derived aquifer recharge exceeds the observed increase in Tanana River base flow. Our findings suggest a linkage between glacier wastage, aquifer recharge along the headwater stream corridor, and lowland winter discharge. Accordingly, glacierized headwater streambeds may serve as major aquifer recharge zones in semiarid climates and therefore contributing to year‐round base flow of lowland rivers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.321
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations63
Published2017
Admission routes1
Has abstractyes

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