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Record W2794985864 · doi:10.1002/2018gl077536

Spatially Resolved Isotopic Source Signatures of Wetland Methane Emissions

2018· article· en· W2794985864 on OpenAlexaff
Anita L. Ganesan, Angharad C. Stell, Nicola Gedney, Edward Comyn‐Platt, Garry Hayman, Matthew Rigby, Benjamin Poulter, Edward R. C. Hornibrook

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Environment Research CouncilSight Research UKMet OfficeDepartment for Environment, Food and Rural Affairs, UK GovernmentGordon and Betty Moore Foundation
KeywordsWetlandEnvironmental scienceLatitudeAtmospheric sciencesEcosystemMethaneIsotopic signatureGeologyStable isotope ratioPhysicsEcologyGeodesy

Abstract

fetched live from OpenAlex

Abstract We present the first spatially resolved wetland δ 13 C(CH 4 ) source signature map based on data characterizing wetland ecosystems and demonstrate good agreement with wetland signatures derived from atmospheric observations. The source signature map resolves a latitudinal difference of ~10‰ between northern high‐latitude (mean −67.8‰) and tropical (mean −56.7‰) wetlands and shows significant regional variations on top of the latitudinal gradient. We assess the errors in inverse modeling studies aiming to separate CH 4 sources and sinks by comparing atmospheric δ 13 C(CH 4 ) derived using our spatially resolved map against the common assumption of globally uniform wetland δ 13 C(CH 4 ) signature. We find a larger interhemispheric gradient, a larger high‐latitude seasonal cycle, and smaller trend over the period 2000–2012. The implication is that erroneous CH 4 fluxes would be derived to compensate for the biases imposed by not utilizing spatially resolved signatures for the largest source of CH 4 emissions. These biases are significant when compared to the size of observed signals.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.999

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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 designBench or experimental
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

Citations95
Published2018
Admission routes1
Has abstractyes

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