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Record W4213289348 · doi:10.1038/s43247-022-00367-z

Improved prediction of the vertical distribution of ground ice in Arctic-Antarctic permafrost sediments

2022· article· en· W4213289348 on OpenAlexafffund
Denis Lacelle, David Fisher, Marjolaine Verret, Wayne H. Pollard

Bibliographic record

VenueCommunications Earth & Environment · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermafrostArcticFrost (temperature)GeologyThermokarstSedimentEnvironmental sciencePhysical geographyHydrology (agriculture)GeomorphologyOceanography

Abstract

fetched live from OpenAlex

Abstract Global warming and permafrost degradation are impacting landscapes, ecosystems and the climate-carbon system. Current ground ice and geohazard maps rely on the frost susceptibility of surficial sediments, and substantial areas underestimate ice abundance. Here we use a soil environmental model to show the importance of considering unfrozen water content (dependent on sediment type, soil water chemistry, and temperature) when assessing the frost susceptibility of sediments. Our ensemble modeling of the vertical structure and evolution of ground ice for fine to coarse-grained sediments matches reasonably well with field measurements at sites from the low Arctic to the cold and hyper-arid Dry Valleys of Antarctica. Our modeling indicates a need to re-evaluate how frost-susceptible sediments are identified when mapping ice-rich permafrost landscapes and provides a framework for the development of quantitative estimates of the vertical distribution of ground ice in permafrost sediments at regional scale.

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 score0.018
Threshold uncertainty score0.999

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)0.0020.000

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 head0.034
GPT teacher head0.225
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 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

Citations22
Published2022
Admission routes2
Has abstractyes

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