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Record W4412714310 · doi:10.1088/2515-7620/adf498

Regional wildfire smoke reduces boreal forest carbon uptake

2025· article· en· W4412714310 on OpenAlexafffundabout
Brandon Van Huizen, Daniel Thompson, SOPHIE WILKINSON, Richard M. Petrone, L. Chasmer, Natascha Kljun, Mike Flannigan, K. J. Devito, J. M. Waddington

Bibliographic record

VenueEnvironmental Research Communications · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsMcMaster UniversityThompson Rivers UniversityUniversity of LethbridgeSimon Fraser UniversityCanadian Forest ServiceUniversity of AlbertaNatural Resources CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEnvironmental scienceSmokeEddy covarianceTaigaPhotosynthetically active radiationBorealPrimary productionEcosystemAtmospheric sciencesRadiative forcingCarbon sequestrationCarbon cycleCarbon fibersClimate changePhotosynthesisCarbon dioxideEcologyMeteorologyGeographyChemistryGeologyBiology

Abstract

fetched live from OpenAlex

Abstract While many studies have examined carbon dynamics of boreal ecosystems following wildfire, research on forest-atmosphere carbon fluxes during widespread smoke events from adjacent active wildfires is limited. We examined eddy covariance carbon exchange adjacent to the May 2011 Utikuma Complex wildfire in central Alberta, Canada. Over a one-week period while the wildfire was burning <10 km from the flux footprint of the tower, net ecosystem CO 2 exchange decreased to almost zero, likely due to smoke-related reductions in photosynthetically active radiation greatly diminishing photosynthesis. The smoke event caused a direct reduction in forest CO 2 sequestration by 0.7 Tg CO 2 during the fire period. As the smoke affected area was 120 times greater than the burnt area itself, this additional carbon reduction was equivalent to ~30% of gross carbon emissions from the fire. We argue that smoke-related inhibition of photosynthesis via reduced light availability should be considered when investigating the net impacts of high-intensity boreal wildfires on the net radiative forcing and global carbon balance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.052
GPT teacher head0.341
Teacher spread0.289 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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