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Record W1975910192 · doi:10.1073/pnas.1409316111

Resistance of the boreal forest to high burn rates

2014· article· en· W1975910192 on OpenAlexaff
Jessie Héon, Dominique Arseneault, Marc‐André Parisien

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsCanadian Forest ServiceCenter for Northern StudiesNatural Resources CanadaUniversité du Québec à Rimouski
Fundersnot available
KeywordsTaigaBorealResistance (ecology)Environmental scienceForestryGeographyPhysical geographyEcologyBiologyArchaeology

Abstract

fetched live from OpenAlex

Boreal ecosystems and their large carbon stocks are strongly shaped by extensive wildfires. Coupling climate projections with records of area burned during the last 3 decades across the North American boreal zone suggests that area burned will increase by 30-500% by the end of the 21st century, with a cascading effect on ecosystem dynamics and on the boreal carbon balance. Fire size and the frequency of large-fire years are both expected to increase. However, how fire size and time since previous fire will influence future burn rates is poorly understood, mostly because of incomplete records of past fire overlaps. Here, we reconstruct the length of overlapping fires along a 190-km-long transect during the last 200 y in one of the most fire-prone boreal regions of North America to document how fire size and time since previous fire will influence future fire recurrence. We provide direct field evidence that extreme burn rates can be sustained by a few occasional droughts triggering immense fires. However, we also show that the most fire-prone areas of the North American boreal forest are resistant to high burn rates because of overabundant young forest stands, thereby creating a fuel-mediated negative feedback on fire activity. These findings will help refine projections of fire effect on boreal ecosystems and their large carbon stocks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Citations166
Published2014
Admission routes1
Has abstractyes

Explore more

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