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Record W2286609366 · doi:10.1111/nph.13909

Methane emissions from the trunks of living trees on upland soils

2016· article· en· W2286609366 on OpenAlexaff
Zhiping Wang, Qian Gu, Feng‐Dan Deng, Jianhui Huang, J. Patrick Megonigal, Qiang Yu, Xiao‐Tao Lü, Linghao Li, Scott X. Chang, Yunhai Zhang, Jinchao Feng, Xingguo Han

Bibliographic record

VenueNew Phytologist · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of Alberta
FundersNatural Science Foundation of Beijing MunicipalityNational Natural Science Foundation of ChinaCenter for Advanced Study, University of Illinois at Urbana-ChampaignU.S. Department of Energy
KeywordsEnvironmental scienceSoil waterTemperate rainforestCanopyTree canopyTemperate forestTemperate climateMethanePeatAtmospheric sciencesEcologyEcosystemHydrology (agriculture)BiologySoil scienceGeology

Abstract

fetched live from OpenAlex

Upland forests are traditionally thought to be net sinks for atmospheric methane (CH4 ). In such forests, in situ CH4 fluxes on tree trunks have been neglected relative to soil and canopy fluxes. We measured in situ CH4 fluxes from the trunks of living trees and other surfaces, such as twigs and soils, using a static closed-chamber method, and estimated the CH4 budget in a temperate upland forest in Beijing. We found that the trunks of Populus davidiana emitted large quantities of CH4 during July 2014-July 2015, amounting to mean annual emissions of 85.3 and 103.1 μg m(-2) h(-1) on a trunk surface area basis on two replicate plots. The emission rates were similar in magnitude to those from tree trunks in wetland forests. The emitted CH4 was derived from the heartwood of trunks. On a plot or ecosystem scale, trunk CH4 emissions were equivalent to c. 30-90% of the amount of CH4 consumed by soils throughout the year, with an annual average of 63%. Our findings suggest that wet heartwoods, regardless of rot or not, occur widely in living trees on various habitats, where CH4 can be produced.

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)0.001
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 score0.380
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.001

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 head0.017
GPT teacher head0.241
Teacher spread0.225 · 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

Citations115
Published2016
Admission routes1
Has abstractyes

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