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Record W2072108794 · doi:10.5194/tc-7-167-2013

Environmental controls on the thermal structure of alpine glaciers

2013· article· en· W2072108794 on OpenAlexafffund
Nathaniel Wilson, Gwenn E. Flowers

Bibliographic record

Venue˜The œcryosphere · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsSimon Fraser University
FundersSFU Community Trust Endowment FundNatural Sciences and Engineering Research Council of CanadaSimon Fraser University
KeywordsGlacierMeltwaterTemperate climateFirnGeologyClimate changeThermalEnvironmental scienceGlacier mass balanceSnowAtmospheric sciencesClimatologyPhysical geographyGeomorphologyMeteorologyEcologyGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract. Water entrapped in glacier accumulation zones represents a significant latent heat contribution to the development of thermal structure. It also provides a direct link between glacier environments and thermal regimes. We apply a two-dimensional mechanically-coupled model of heat flow to synthetic glacier geometries in order to explore the environmental controls on flowband thermal structure. We use this model to test the sensitivity of thermal structure to physical and environmental variables and to explore glacier thermal response to environmental changes. In different conditions consistent with a warming climate, mean glacier temperature and the volume of temperate ice may either increase or decrease, depending on the competing effects of elevated meltwater production, reduced accumulation zone extent and thinning firn. For two model reference states that exhibit commonly-observed thermal structures, the fraction of temperate ice is shown to decline with warming air temperatures. Mass balance and aquifer sensitivities play an important role in determining how the englacial thermal regimes of alpine glaciers will adjust in the future.

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 score0.093
Threshold uncertainty score0.967

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)0.0330.000

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 head0.008
GPT teacher head0.171
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.

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
Published2013
Admission routes2
Has abstractyes

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