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Record W2060609159 · doi:10.3189/002214311795306709

Glacier subsurface heat-flux characterizations for energy-balance modelling in the Donjek Range, southwest Yukon, Canada

2011· article· en· W2060609159 on OpenAlexafffundabout
B. A. Wheler, Gwenn E. Flowers

Bibliographic record

VenueJournal of Glaciology · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsSimon Fraser UniversityParks Canada
KeywordsSnowpackGlacierShortwave radiationGeologyMeltwaterEnergy balanceAblation zoneHeat fluxSnowFlux (metallurgy)ShortwaveGeomorphologyEnvironmental scienceHeat transferMechanicsMaterials scienceRadiationThermodynamicsRadiative transfer

Abstract

fetched live from OpenAlex

Abstract We apply a point-scale energy-balance model to a small polythermal glacier in the St Elias Mountains of Canada in order to investigate the applicability and limitations of different treatments of the glacier surface temperature and subsurface heat flux. These treatments range in complexity from a multilayer subsurface model that simulates snowpack evolution, to the assumption of a constant glacier surface temperature equal to 0°C. The most sophisticated model includes dry densification of the snowpack, penetration of shortwave radiation into the subsurface, internal melting, refreezing of percolating meltwater and generation of slush layers. Measurements of subsurface temperature and surface lowering are used for model validation, and highlight the importance of including subsurface penetration of shortwave radiation in the model. Using an iterative scheme to solve for the subsurface heat flux as the residual of the energy-balance equation results in an overestimation of total ablation by 18%, while the multilayer subsurface model underestimates ablation by 6%. By comparison, the 0°C surface assumption leads to an overestimation of ablation of 29% in this study where the mean annual air temperature is about −8°C.

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 score0.429
Threshold uncertainty score0.828

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 head0.041
GPT teacher head0.204
Teacher spread0.163 · 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

Citations35
Published2011
Admission routes3
Has abstractyes

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