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Record W2016720504 · doi:10.1139/x02-077

Environmental controls on soil CO<sub>2</sub> flux following fire in black spruce, white spruce, and aspen stands of interior Alaska

2002· article· en· W2016720504 on OpenAlexvenueno aff
Katherine O’Neill, Eric S. Kasischke

Bibliographic record

VenueCanadian Journal of Forest Research · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsBlack spruceEnvironmental scienceTaigaPermafrostCarbon sinkSoil carbonSoil waterWater contentSink (geography)MoistureEcosystemAgronomyForestrySoil scienceEcologyChemistryGeologyBiologyGeography

Abstract

fetched live from OpenAlex

Boreal forests contain large amounts of stored soil carbon and are susceptible to periodic disturbance by wildfire. This study evaluates the relationship between post-fire changes in soil temperature, moisture, and CO 2 exchange in paired burned and control stands of three Alaskan forest systems: Picea mariana (Mill.) BSP, Picea glauca (Moench) Voss, and Populus tremuloides Michx. In these systems, the environmental factor that most directly controlled rates of carbon exchange varied depending upon burn status and soil drainage. In mature unburned stands, CO 2 flux was highly correlated with seasonal patterns of soil temperature. Following fire, these soils became significantly warmer, and carbon exchange became more sensitive to fluctuations in surface moisture conditions. The effect of fire on soil climate was most pronounced in the P. mariana stands, which experienced a mean seasonal temperature increase of 5–8°C in the upper 1 m of the soil profile, a 200% increase in the rate of active layer thaw, and a reduction in mean surface moisture potential. Evidence from soil CO 2 profiles suggests that these environmental changes may have resulted in enhanced decomposition of carbon previously immobilized by permafrost, potentially transforming a landscape that was once a net sink for carbon into a carbon source.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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

Citations102
Published2002
Admission routes1
Has abstractyes

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