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Record W2151989051 · doi:10.1002/hyp.6123

Boundary‐layer growth over snow and soil patches: field observations

2006· article· en· W2151989051 on OpenAlexafffundabout
R. J. Granger, Richard Essery, John W. Pomeroy

Bibliographic record

VenueHydrological Processes · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of SaskatchewanEnvironment and Climate Change Canada
FundersNatural Environment Research CouncilCanadian Foundation for Climate and Atmospheric SciencesUniversity of Saskatchewan
KeywordsSnowBoundary layerSnowmeltAdvectionAtmospheric sciencesSurface finishSurface roughnessGeologyPlanetary boundary layerEnvironmental scienceMeteorologyMechanicsGeomorphologyMaterials scienceGeographyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract Much of the snowmelt season is characterized by a patchy surface; differential heating of the snow and snow‐free surfaces results in a significant horizontal transport of energy that affects and contributes to the snowmelt. The calculation of the rate of energy advection requires some knowledge of the behaviour of the thermal boundary layer over the patches of snow and snow‐free surfaces. We present the results from a series of field observations of the rate of growth of the thermal boundary layer over snow and snow‐free patches. The results confirm that the boundary‐layer growth can be described by a power function of the distance from the leading edge of the patch. For the case of the thermal boundary layer over a snow patch within a bare field, the boundary‐layer growth is affected by the upwind surface roughness; the thermal boundary layer over a snow patch within a ‘rough’ field grows much more quickly than that in a ‘smooth’ field. Relationships are derived and presented for the parameterization of the boundary‐layer growth as a function of distance and upwind surface roughness. Copyright © 2006 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

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 score0.030
Threshold uncertainty score1.000

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)0.0010.000

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 head0.030
GPT teacher head0.219
Teacher spread0.188 · 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

Citations42
Published2006
Admission routes3
Has abstractyes

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