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Record W4417209969 · doi:10.1088/2515-7620/ae2b53

The thermal conductivity of vegetation ground covers in permafrost areas

2025· article· en· W4417209969 on OpenAlexafffundabout
Konstantin Ozeritskiy, Jocelyn L. Hayley

Bibliographic record

VenueEnvironmental Research Communications · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostThermal conductivityVegetation (pathology)Hydrology (agriculture)BiomeThermalMoistureWater content

Abstract

fetched live from OpenAlex

Abstract Cold biomes are characterized by bryophyte-dominated vegetation, which helps regulate soil temperature by altering thermal conductivity based on moisture content. However, information on the thermal properties of vegetation ground covers in permafrost areas, particularly in their frozen state, remains limited. This study aims to fill this gap by investigating the thermal conductivity of various moss and lichen species collected from Northern Manitoba’s Hudson Bay lowlands, using both field and laboratory measurements. The results show that, in unfrozen conditions, thermal conductivity increases linearly with volumetric water content. In frozen samples, an exponential relationship was observed, indicating an increase in conductivity as water turns to ice. We derived empirical equations to describe these relationships and validated them using one-dimensional thermal modeling at instrumented permafrost sites. We also tested the transferability of our approach at a comparative site in the Yamal region and explored the thermal impacts of vegetation removal. The findings highlight the importance of including vegetation ground cover properties in permafrost thermal modeling and provide a practical framework for quantifying their effects under both natural and disturbed conditions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.096
GPT teacher head0.349
Teacher spread0.252 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes3
Has abstractyes

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