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Record W2128612888 · doi:10.1029/2008gl034031

Emissions of CH<sub>4</sub> and N<sub>2</sub>O over the United States and Canada based on a receptor‐oriented modeling framework and COBRA‐NA atmospheric observations

2008· article· en· W2128612888 on OpenAlexaboutno aff
E. A. Kort, J. Eluszkiewicz, Britton B. Stephens, J. B. Miller, Christoph Gerbig, Thomas Nehrkorn, Bruce C. Daube, Jed O. Kaplan, Sander Houweling, Steven C. Wofsy

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersNOAA ResearchU.S. Department of DefenseNational Defense Science and Engineering GraduateNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsGreenhouse gasNitrous oxideEnvironmental scienceMethaneAtmospheric sciencesLagrangianAtmospheric dispersion modelingDispersion (optics)ScalingMeteorologyAir pollutionPhysicsChemistryGeologyMathematicsOceanography

Abstract

fetched live from OpenAlex

We present top‐down emission constraints for two non‐CO 2 greenhouse gases in large areas of the U.S. and southern Canada during early summer. Collocated airborne measurements of methane and nitrous oxide acquired during the COBRA‐NA campaign in May–June 2003, analyzed using a receptor‐oriented Lagrangian particle dispersion model, provide robust validation of independent bottom‐up emission estimates from the EDGAR and GEIA inventories. We find that the EDGAR CH 4 emission rates are slightly low by a factor of 1.08 ± 0.15 (2 σ ), while both EDGAR and GEIA N 2 O emissions are significantly too low, by factors of 2.62 ± 0.50 and 3.05 ± 0.61, respectively, for this region. Potential footprint bias may expand the statistically retrieved uncertainties. Seasonality of agricultural N 2 O emissions may help explain the discrepancy. Total anthropogenic U.S. and Canadian emissions would be 49 Tg CH 4 and 4.3 Tg N 2 O annually, if these inventory scaling factors applied to all of North America.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations175
Published2008
Admission routes1
Has abstractyes

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