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Record W2058522703 · doi:10.1038/ngeo1955

Three decades of global methane sources and sinks

2013· article· en· W2058522703 on OpenAlexaff
S. Kirschke, Philippe Bousquet, Philippe Ciais, Marielle Saunois, Josep G. Canadell, E. J. Dlugokencky, P. Bergamaschi, D. Bergmann, D. R. Blake, Lori Bruhwiler, Philip Cameron‐Smith, Simona Castaldi, Frédéric Chevallier, Liang Feng, A. Fraser, Martin Heimann, E. L. Hodson, Sander Houweling, Béatrice Josse, Paul J. Fraser, Paul B. Krummel, Jean‐François Lamarque, R. L. Langenfelds, Corinne Le Quéré, Vaishali Naïk, Simon O’Doherty, Paul I. Palmer, Isabelle Pison, David A. Plummer, Benjamin Poulter, Ronald G. Prinn, Matthew Rigby, Bruno Ringeval, Monia Santini, Martina Schmidt, Drew Shindell, Isobel J. Simpson, Renato Spahni, Lloyd Steele, Sarah A. Strode, Kengo Sudo, Sophie Szopa, Guido R. van der Werf, Apostolos Voulgarakis, Michiel van Weele, Ray F. Weiss, J. E. Williams, Guang Zeng

Bibliographic record

VenueNature Geoscience · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of VictoriaEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric AdministrationSight Research UKGrand Équipement National De Calcul IntensifEuropean CommissionNational Energy Research Scientific Computing CenterNational Centre for Earth ObservationNatural Environment Research CouncilU.S. Department of Energy
KeywordsMethaneAtmospheric methaneGreenhouse gasEnvironmental scienceAtmospheric sciencesEcosystemMethane emissionsFossil fuelGreenhouse effectTroposphereGlobal warmingClimate changeChemistryEcologyGeology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0030.007
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.003
GPT teacher head0.204
Teacher spread0.201 · 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

Citations2,368
Published2013
Admission routes1
Has abstractno

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