MétaCan
Menu
Back to cohort
Record W1994453594 · doi:10.5194/bg-6-2355-2009

The consumption of atmospheric methane by soil in a simulated future climate

2009· article· en· W1994453594 on OpenAlexafffund
Charles L. Curry

Bibliographic record

VenueBiogeosciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsEnvironmental scienceAtmospheric sciencesTemperate climateTundraWater contentMethaneSink (geography)LatitudeSubtropicsAtmospheric methaneSnowEcosystemEcologyGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract. A recently developed model for the consumption of atmospheric methane by soil (Curry, 2007) is used to investigate the global magnitude and distribution of methane uptake in a simulated future climate. In addition to solving the one-dimensional diffusion-reaction equation, the model includes a parameterization of biological CH4 oxidation that is sensitive to soil temperature and moisture content, along with specified reduction factors for land cultivation and wetland fractional coverage. Under the SRES emission scenario A1B, the model projects an 8% increase in the global annual mean CH4 soil sink by 2100, over and above the 15% increase expected from increased CH4 concentration alone. While the largest absolute increases occur in cool temperate and subtropical forest ecosystems, the largest relative increases in consumption (>40%) are seen in the boreal forest, tundra and polar desert environments of the high northern latitudes. Methane uptake at mid- to high northern latitudes increases year-round in 2100, with a 68% increase over present-day values in June. This increase is primarily due to enhanced soil diffusivity resulting from lower soil moisture produced by increased evaporation and reduced snow cover. At lower latitudes, uptake is enhanced mainly by elevated soil temperatures and/or reduced soil moisture stress, with the dominant influence determined by the local climate.

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)0.000
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 score0.088
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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

Citations52
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueBiogeosciencesSame topicAtmospheric and Environmental Gas DynamicsFrench-language works237,207