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Record W1853943398 · doi:10.1111/1365-2664.12136

REVIEW: Charcoal function and management in boreal ecosystems

2013· article· en· W1853943398 on OpenAlexafffund
Stephen A. Hart, Nancy Luckai

Bibliographic record

VenueJournal of Applied Ecology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsCharcoalBorealEcosystemEnvironmental scienceFunction (biology)EcologyTaigaGeographyAgroforestryEnvironmental resource managementBiologyChemistryEvolutionary biology

Abstract

fetched live from OpenAlex

Summary Charcoal plays an important role in soil function and carbon storage in fire‐prone ecosystems. Charcoal is present in most boreal forest soils as a result of naturally recurring wildfires, which convert 0·7–2% of biomass to charcoal. In boreal forests, charcoal represents 8–10% of soil carbon and 1 pg of carbon globally. Charcoal is resistant to decay, representing a form of super‐passive carbon, with half‐lives one to two orders of magnitude greater than those of other soil organic matter. High concentrations of negative surface charges increase nutrient retention, impacting boreal soil function, productivity and species composition. Due to a lack of soil mixing processes, charcoal in boreal soils is vulnerable to recombustion in recurring fires, inhibiting the accumulation of charcoal over time, unlike in other fire‐prone ecosystems. Boreal charcoal stocks are highly variable. Increased fire intensity results in greater charcoal formation, with stand‐replacing crown fires resulting in much larger charcoal stocks than non‐stand‐replacing ground fires. Current estimates of carbon storage based on Scandinavian studies of non‐stand‐replacing fires may underestimate charcoal stocks by factors of 2–3. Synthesis and applications . Charcoal contributes to boreal soil function, ecosystem productivity, nutrient retention and carbon cycling. In the absence of fire, charcoal loses many active properties, contributing to declining productivity with increasing time since fire. Incorporation of charcoal into ecosystem management using prescribed burns may contribute to sustainable management of boreal forests and maintaining global carbon cycles.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.004
GPT teacher head0.197
Teacher spread0.192 · 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.

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

Citations43
Published2013
Admission routes2
Has abstractyes

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