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Record W2124329346 · doi:10.1071/wf14167

Fire in arctic tundra of Alaska: past fire activity, future fire potential, and significance for land management and ecology

2015· article· en· W2124329346 on OpenAlexaff
Nancy H. F. French, Liza K. Jenkins, Tatiana Loboda, Randi Jandt, Laura Bourgeau‐Chavez, Matthew Whitley

Bibliographic record

VenueInternational Journal of Wildland Fire · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTundraFire regimeArcticVegetation (pathology)Context (archaeology)Environmental scienceClimate changeGeographyPhysical geographyBorealFire protectionTaigaEcologyEcosystemClimatologyForestryGeology

Abstract

fetched live from OpenAlex

A multidecadal analysis of fire in Alaskan Arctic tundra was completed using records from the Alaska Large Fire Database. Tundra vegetation fires are defined by the Circumpolar Arctic Vegetation Map and divided into five tundra ecoregions of Alaska. A detailed review of fire records in these regions is presented, and an analysis of future fire potential was performed based on future climate scenarios. The average size of tundra fire based on the data record is 22 km2 (5454 acres). Fires show a mean size of 10 km2 (2452 acres) and median of 0.064 km2 (16 acres), indicating small fires are common. Although uncommon, 16 fires larger than 300 km2 (74 132 acres) have been recorded across four ecoregions and all five decades. Warmer summers with extended periods of drying are expected to increase fire activity as indicated by fire weather index. The implications of the current fire regime and potential changes in fire regime are discussed in the context of land management and ecosystem services. Current fire management practices and land-use planning in Alaska should be specifically tailored to the tundra region based on the current fire regime and in anticipation of the expected change in fire regime projected with climate change.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.356

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)0.0000.000

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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations80
Published2015
Admission routes1
Has abstractyes

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