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Record W2398233130 · doi:10.1002/2016gl068782

The contribution of an overlooked transport process to a wetland's methane emissions

2016· article· en· W2398233130 on OpenAlexfundno aff
C. Poindexter, Dennis Baldocchi, Jaclyn Hatala Matthes, Sara Knox, Evan Variano

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersPhilomathia FoundationDepartment of Water ResourcesU.S. Department of Energy
KeywordsMethaneWetlandEnvironmental scienceGreenhouse gasTemperate climateAtmosphere (unit)Atmospheric sciencesAtmospheric methaneMarshConvectionMethanogenesisHydrology (agriculture)EcologyGeologyMeteorologyPhysicsOceanography

Abstract

fetched live from OpenAlex

Abstract Wetland methane transport processes affect what portion of methane produced in wetlands reaches the atmosphere. We model what has been perceived to be the least important of these transport processes: hydrodynamic transport of methane through wetland surface water and show that its contribution to total methane emissions from a temperate freshwater marsh is surprisingly large. In our 1 year study, hydrodynamic transport comprised more than half of nighttime methane fluxes and was driven primarily by water column thermal convection occurring overnight as the water surface cooled. Overall, hydrodynamic transport was responsible for 32% of annual methane emissions. Many methane models have overlooked this process, but our results show that wetland methane fluxes cannot always be accurately described using only other transport processes (plant‐mediated transport and ebullition). Modifying models to include hydrodynamic transport and the mechanisms that drive it, particularly convection, could help improve predictions of future wetland methane emissions.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.314
Teacher spread0.296 · 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

Citations62
Published2016
Admission routes1
Has abstractyes

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