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Record W2345799969 · doi:10.1680/jenge.15.00058

Heat transfer within frozen slopes in subarctic Yukon, Canada

2016· article· en· W2345799969 on OpenAlexafffundabout
Joel T Steeves, S. Lee Barbour, Grant Ferguson, Sean K. Carey

Bibliographic record

VenueEnvironmental Geotechnics · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcMaster UniversityInro Consultants (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeologyThermal conductionSnowmeltPermafrostConvectionSnowHeat transferGeomorphologySoil waterGeotechnical engineeringSubarctic climateGroundwaterHydrology (agriculture)Soil scienceMechanicsMaterials science

Abstract

fetched live from OpenAlex

The dominant mechanism of heat transfer during ground thaw is typically assumed to be vertical conduction. However, the addition of lateral subsurface water flow introduces the potential for the forced convection of energy, having an influence on ground temperatures and thaw rates. Field observations of snowmelt run-off and rates of ground thaw for two slopes within the Wolf Creek basin, Yukon, Canada, highlighted different rates of ground thaw with slope position. Ground temperatures were numerically simulated to evaluate the relative influence of conduction and convection on the thawing of these slopes; each slope comprised different soils and had a different slope aspect. Both slopes were composed of an organic layer overlying a mineral soil. Lateral water flow above the frozen layer occurred within both slopes as a result of a perched saturated zone above the organic–mineral interface. The numerical models reveal that lateral diversion within a surficial, high-hydraulic conductivity layer, such as an organic layer, can initiate convective heat transfer. However, the observed differential thaw was determined to be a result of conduction and variations in the initial ice content and snow cover rather than lateral convection of heat.

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 score0.451
Threshold uncertainty score0.988

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)0.0120.000

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 head0.011
GPT teacher head0.169
Teacher spread0.158 · 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

Citations2
Published2016
Admission routes3
Has abstractyes

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