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Record W2159234944 · doi:10.3189/002214310794457254

Identification and characterization of alpine subglacial lakes using interferometric synthetic aperture radar (InSAR): Brady Glacier, Alaska, USA

2010· article· en· W2159234944 on OpenAlexafffund
Denny M. Capps, Bernhard Rabus, John J. Clague, D. H. Shugar

Bibliographic record

VenueJournal of Glaciology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Space Agency
KeywordsGlacierGeologyInterferometric synthetic aperture radarSynthetic aperture radarGeomorphologySubsidencePhysical geographyGlacier mass balanceRadarTidewater glacier cycleRemote sensingIce calvingStructural basinGeography

Abstract

fetched live from OpenAlex

Abstract The temporary storage and subsequent release of water at glacial margins can cause severe flooding in downstream areas and substantially impact glacier dynamics. Alpine subglacial lakes may not be identified until they become subaerially exposed or release a jokulhlaup. We use interferometric synthetic aperture radar (InSAR) to identify and characterize three dynamic alpine subglacial lakes of Brady Glacier, Alaska, USA. We quantify changes in vertical displacement of the glacier surface and lake volumes from September 1995 through March 1996 using European Remote-sensing Satellite-1/-2 (ERS- 1/-2) tandem data. In the autumn, subsidence ranged from 4 to 26cmd -1 and the volume of water discharged ranged from 22 000 ± 2000 to 243 000 ± 14 000m 3 d -1 . Subsidence and discharge rates declined significantly during the winter and continued at a lesser rate through March. Application of this technique may allow researchers to locate alpine subglacial lakes years or decades before they begin to release hazardous outburst floods and substantially impact glacier dynamics.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.506

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)0.0000.000

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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

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