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Record W2135503951 · doi:10.1002/2015jf003679

Spatiotemporal impacts of wildfire and climate warming on permafrost across a subarctic region, Canada

2015· article· en· W2135503951 on OpenAlexaffabout
Yu Zhang, S A Wolfe, Peter Morse, Ian Olthof, Robert Fraser

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsGeological Survey of CanadaNatural Resources Canada
Fundersnot available
KeywordsPermafrostSubarctic climateTundraActive layerEnvironmental scienceBogGlobal warmingClimate changeTaigaPhysical geographyClimatologyAtmospheric sciencesArcticPeatGeologyEcologyGeographyLayer (electronics)ForestryOceanography

Abstract

fetched live from OpenAlex

Abstract Field observations show significant impacts of wildfires on active layer thickness and ground temperatures. However, the importance of fires to permafrost conditions at regional scales remains unclear, especially with climate warming. This study evaluated the regional impacts of fire on permafrost with climate change from 1942 to 2100 using a process‐based model in a large subarctic region in the Northwest Territories, Canada. Climate warming is shown to be the dominant factor for permafrost reduction. The warming trend of climate reduces permafrost extent in this region from 67% at present to 2% by 2100. For burned areas, fire increases the reduction of permafrost extent by up to 9% on average, with up to 16% for forest, 10% for tundra and bogs, and 4% for fens. Fire accelerates permafrost disappearance by 5 years on average. The effects of fire on active layer thickness and permafrost extent are much larger in forest areas than in tundra, bogs, and fens. Since active layer is thicker after a fire and cannot recover in most of the areas, the fire effects on active layer are widespread. On average, fires thickens active layer by about 0.5 m. The fire effects on active layer increased significantly after 1990 due to climate warming.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.117
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.098
GPT teacher head0.337
Teacher spread0.239 · 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

Citations70
Published2015
Admission routes2
Has abstractyes

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