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Record W2512038744 · doi:10.1088/1748-9326/11/9/095001

More frequent burning increases vulnerability of Alaskan boreal black spruce forests

2016· article· en· W2512038744 on OpenAlexaff
Elizabeth Hoy, Merritt R. Turetsky, Eric S. Kasischke

Bibliographic record

VenueEnvironmental Research Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Guelph
FundersNational Aeronautics and Space Administration
KeywordsEnvironmental scienceBlack spruceTaigaPermafrostSoil carbonEcosystemBorealFire regimeDeciduousForestrySoil waterEcologySoil scienceGeographyBiology

Abstract

fetched live from OpenAlex

Much recent research has investigated the effects of burning on mature black spruce ( Picea mariana ) forests in interior Alaska, however little research has focused on how frequent reburning affects soil organic layer (SOL) vulnerability in these ecosystems. We compared organic soil layer characteristics in black spruce stands that burned after two fire-free intervals (FFI), including an intermediate-interval (37–52 years) and a more typical long-interval (70–120 years). We found that depth of burn varied significantly between intermediate-interval and long-interval sites, and as there was less material available to burn in intermediate-interval stands, percent depth reduction was greater in these stands (78.9% ± 2.6%) than in long-interval stands (62.9% ± 1.5%). As a result, less residual organic soil carbon remained post-fire in intermediate-interval than long-interval stands. Post-fire organic soil carbon stocks averaged 0.51 ± 0.08 kg C m ^−2 in the intermediate-interval sites, which is less than estimates of soil carbon stock for long-interval fire events (ranging from 2.07 to 5.74 kg C m ^−2 ). In addition to altering soil carbon storage, a depletion of the SOL during more frequent fire events will likely delay the recovery of permafrost and could trigger a change in the possible successional trajectory of a site, from black spruce dominated to deciduous or even shrub dominated ecosystems in the future.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.017
GPT teacher head0.282
Teacher spread0.265 · 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; both teacher heads agree on what is shown here.

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

Citations49
Published2016
Admission routes1
Has abstractyes

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