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Record W1569776337 · doi:10.1002/jgrf.20045

Spatio‐temporal features of permafrost thaw projected from long‐term high‐resolution modeling for a region in the Hudson Bay Lowlands in Canada

2013· article· en· W1569776337 on OpenAlexaffabout
Yu Zhang

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsPermafrostPhysical geographyClimate changeGeologyLatitudeStage (stratigraphy)Global warmingClimatologyEnvironmental scienceGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract Although studies agree that climate warming will cause permafrost thaw, projected permafrost conditions differ widely, and most projections use half degree latitude/longitude or coarser spatial resolution. Using a process‐based model, this study projected changes of permafrost from 2010 to 2200 at 30 m by 30 m resolution for a region in the northwest of the Hudson Bay Lowlands in Canada. This long‐term spatially detailed modeling revealed some general features of permafrost dynamics with climate warming. Temporally, permafrost degradation at a site can be divided into five stages: gradual‐thawing stage, increased‐thawing stage, frequent‐talik stage, isothermal‐permafrost stage, and permafrost‐free stage. This study determined the beginning or ends of the stages for each grid cell and mapped the degradation stages in this region. Spatially, permafrost was predicted to become increasingly discontinuous with climate warming. By the end of the 22nd century, only 20% to 65% of the land area in this region will be underlain by permafrost. With the formation of taliks, the maximum summer thaw depth will increase significantly, and near‐surface permafrost will disappear in many areas while permafrost at depth can persist for decades. Thus, the spatial distribution of near‐surface permafrost and permafrost at depth can be very different. This study also shows that climate scenarios, the depth of permafrost considered, spatial resolution and associated ground conditions used for modeling could cause significant differences in permafrost projections.

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 score0.094
Threshold uncertainty score0.351

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.298
Teacher spread0.222 · 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

Citations28
Published2013
Admission routes2
Has abstractyes

Explore more

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