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Record W2884461525 · doi:10.1038/s41467-018-05457-1

Wildfire as a major driver of recent permafrost thaw in boreal peatlands

2018· article· en· W2884461525 on OpenAlexafffundabout
Carolyn Gibson, L. Chasmer, Dan K. Thompson, William L. Quinton, Mike Flannigan, David Olefeldt

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsNatural Resources CanadaWilfrid Laurier UniversityCanadian Forest ServiceUniversity of LethbridgeUniversity of Alberta
FundersAlberta InnovatesUniversity of Alberta
KeywordsPermafrostThermokarstPeatBogEnvironmental scienceBorealClimate changeTaigaActive layerPhysical geographyHydrology (agriculture)Global warmingEarth scienceGeologyEcologyGeographyForestryOceanographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Permafrost vulnerability to climate change may be underestimated unless effects of wildfire are considered. Here we assess impacts of wildfire on soil thermal regime and rate of thermokarst bog expansion resulting from complete permafrost thaw in western Canadian permafrost peatlands. Effects of wildfire on permafrost peatlands last for 30 years and include a warmer and deeper active layer, and spatial expansion of continuously thawed soil layers (taliks). These impacts on the soil thermal regime are associated with a tripled rate of thermokarst bog expansion along permafrost edges. Our results suggest that wildfire is directly responsible for 2200 ± 1500 km 2 (95% CI) of thermokarst bog development in the study region over the last 30 years, representing ~25% of all thermokarst bog expansion during this period. With increasing fire frequency under a warming climate, this study emphasizes the need to consider wildfires when projecting future circumpolar permafrost thaw.

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 score0.286
Threshold uncertainty score0.997

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.302
Teacher spread0.264 · 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

Citations324
Published2018
Admission routes3
Has abstractyes

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