MétaCan
Menu
Back to cohort
Record W4284714474 · doi:10.1038/s41561-022-00979-2

Permafrost cooled in winter by thermal bridging through snow-covered shrub branches

2022· article· en· W4284714474 on OpenAlexafffundabout
Florent Dominé, Kévin Fourteau, Ghislain Picard, Georg Lackner, Denis Sarrazin, Mathilde Poirier

Bibliographic record

VenueNature Geoscience · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversité LavalMakivik Corporation
FundersInstitut Polaire Français Paul Emile VictorBNP Paribas CardifCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaFondation BNP ParibasNatural Sciences and Engineering Research Council of CanadaEuropean CommissionParks Canada
KeywordsPermafrostSnowShrubBridging (networking)GeologyPhysical geographyEarth scienceSnow coverEnvironmental scienceClimatologyGeomorphologyGeographyOceanographyEcology

Abstract

fetched live from OpenAlex

Considerable expansion of shrubs across the Arctic tundra has been observed in recent decades. These shrubs are thought to have a warming effect on permafrost by increasing snowpack thermal insulation, thereby limiting winter cooling and accelerating thaw. Here, we use ground temperature observations and heat transfer simulations to show that low shrubs can actually cool the ground in winter by providing a thermal bridge through the snowpack. Observations from unmanipulated herb tundra and shrub tundra sites on Bylot Island in the Canadian high Arctic reveal a 1.21 °C cooling effect between November and February. This is despite a snowpack that is twice as insulating in shrubs. The thermal bridging effect is reversed in spring when shrub branches absorb solar radiation and transfer heat to the ground. The overall thermal effect is likely to depend on snow and shrub characteristics and terrain aspect. The inclusion of these thermal bridging processes into climate models may have an important impact on projected greenhouse gas emissions by permafrost.

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 score0.074
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
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.014
GPT teacher head0.233
Teacher spread0.220 · 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

Citations53
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueNature GeoscienceSame topicClimate change and permafrostFrench-language works237,207