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Record W3042333930 · doi:10.1029/2020wr027638

A Semianalytical Interface Model of Soil Freeze/Thaw and Permafrost Evolution

2020· article· en· W3042333930 on OpenAlexafffund
Élise Devoie, James R. Craig

Bibliographic record

VenueWater Resources Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Waterloo
FundersPolar Knowledge Canada
KeywordsPermafrostActive layerInfiltration (HVAC)Non-equilibrium thermodynamicsSoil scienceSoil waterBoundary value problemGeologyPorous mediumHeat transferGeotechnical engineeringPorosityMechanicsEnvironmental scienceGeomorphologyMaterials scienceThermodynamicsLayer (electronics)Physics

Abstract

fetched live from OpenAlex

Abstract A physically based one‐dimensional sharp‐interface model of active layer evolution and permafrost thaw is presented. This computationally efficient, semianalytical, nonequilibrium solution to soil freeze‐thaw problems in partially saturated media is proposed as a component of hydrological models to describe seasonal ground ice, active layer evolution, and changes in permafrost temperature and extent. The model is developed and validated against the analytical Stefan solution and a finite volume coupled heat and mass transfer model of freeze‐thaw in unsaturated porous media. Unlike analytic models, the interface model provides a nonequilibrium solution to the heat equation while permitting a wide range of temporally variable boundary conditions and supporting the simulation of multiple interfaces between frozen and unfrozen soils. The model is implemented for use in discontinuous permafrost peatlands where soil properties are highly dependent on soil ice content and infiltration capacity is high. It is demonstrated that the model is suitable for the representation of variably saturated active layer and permafrost evolution in cases both with and without a talik.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.120
GPT teacher head0.311
Teacher spread0.191 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations16
Published2020
Admission routes2
Has abstractyes

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