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Record W2981950071 · doi:10.1002/ppp.2031

A model of unfrozen water content and its transport in icy permafrost soils: Effects on ground ice content and permafrost stability

2019· article· en· W2981950071 on OpenAlexafffund
David Fisher, Denis Lacelle, Wayne H. Pollard

Bibliographic record

VenuePermafrost and Periglacial Processes · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostSoil waterWater contentGeologySoil scienceEnvironmental scienceAtmospheric sciencesHydrology (agriculture)GeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Knowledge of the amount of unfrozen water and its migration in permafrost soils is important for understanding physico‐chemical and biological processes. Here, we developed sub‐routines in FREZCHEM and embedded them in the WATEREGO2 soil environmental model to: (a) estimate unfrozen water content under changing soil temperatures and water–ice phase changes; and (b) determine the effects of Van der Waals (VdW) and rheological forces driven by seasonal temperature variations on the transport of residual water and the long‐term evolution of ground ice content over depths of 30 m. Together, the seasonal thermal regime and associated VdW and rheological forces on the transport of residual water lead to the evolution of distinct zones of ice‐enrichment: near the surface of permafrost, at 3–5 m, 11–13 m and 17–19 m depth. The depths of ice enrichment are a function of soil thermal diffusivity, and the time needed to evolve the ground ice content is dependent on soil type, soil water chemistry and permafrost temperature. The model can explain observed variations with depth in ground ice content of icy permafrost soils and indicate that these conditions evolve over time. The findings can be used to assess the stability of permafrost to climate change under different temperature scenarios.

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 categoriesMeta-epidemiology (narrow), Insufficient 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 score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.076
GPT teacher head0.241
Teacher spread0.165 · 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

Citations33
Published2019
Admission routes2
Has abstractyes

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