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Record W3155495511 · doi:10.1126/science.abf3903

Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees

2021· article· en· W3155495511 on OpenAlexaff
Michelle C. Mack, Xanthe J. Walker, Jill F. Johnstone, Heather D. Alexander, April M. Melvin, Mélanie Jean, Samantha Miller

Bibliographic record

VenueScience · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité de MonctonYukon UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsDeciduousTaigaDominance (genetics)BorealOffset (computer science)Environmental scienceCarbon stockEcologyForestryPhysical geographyGeographyAgroforestryAtmospheric sciencesBiologyClimate changeGeologyComputer science

Abstract

fetched live from OpenAlex

In boreal forests, climate warming is shifting the wildfire disturbance regime to more frequent fires that burn more deeply into organic soils, releasing sequestered carbon to the atmosphere. To understand the destabilization of carbon storage, it is necessary to consider these effects in the context of long-term ecological change. In Alaskan boreal forests, we found that shifts in dominant plant species catalyzed by severe fire compensated for greater combustion of soil carbon over decadal time scales. Severe burning of organic soils shifted tree dominance from slow-growing black spruce to fast-growing deciduous broadleaf trees, resulting in a net increase in carbon storage by a factor of 5 over the disturbance cycle. Reduced fire activity in future deciduous-dominated boreal forests could increase the tenure of this carbon on the landscape, thereby mitigating the feedback to climate warming.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.198
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations281
Published2021
Admission routes1
Has abstractyes

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