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Record W2161514023 · doi:10.1029/2005gl025403

Sensitivity of inverse estimation of annual mean CO<sub>2</sub> sources and sinks to ocean‐only sites versus all‐sites observational networks

2006· article· en· W2161514023 on OpenAlexaff
Prabir K. Patra, K. R. Gurney, Scott Denning, Shamil Maksyutov, Takakiyo Nakazawa, D. F. Baker, Philippe Bousquet, Lori Bruhwiler, Yuhan Chen, Philippe Ciais, Songmiao Fan, Inez Fung, Manuel Gloor, Martin Heimann, Kaz Higuchi, Jasmin G. John, R. M. Law, T. Mäki, Bernard Pak, Philippe Peylin, Michael J. Prather, P. J. Rayner, Jorge L. Sarmiento, Shoichi Taguchi, Taro Takahashi, Chiu‐Wai Yuen

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsEnvironmental scienceSensitivity (control systems)Flux (metallurgy)Range (aeronautics)Atmospheric sciencesAtmospheric modelsInverseClimatologyMeteorologyAtmosphere (unit)GeologyMathematicsGeographyChemistry

Abstract

fetched live from OpenAlex

Inverse estimation of carbon dioxide (CO 2 ) sources and sinks uses atmospheric CO 2 observations, mostly made near the Earth's surface. However, transport models used in such studies lack perfect representation of atmospheric dynamics and thus often fail to produce unbiased forward simulations. The error is generally larger for observations over the land than those over the remote/marine locations. The range of this error is estimated by using multiple transport models (16 are used here). We have estimated the remaining differences in CO 2 fluxes due to the use of ocean‐only versus all‐sites (i.e., over ocean and land) observations of CO 2 in a time‐independent inverse modeling framework. The fluxes estimated using the ocean‐only networks are more robust compared to those obtained using all‐sites networks. This makes the global, hemispheric, and regional flux determination less dependent on the selection of transport model and observation network.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.596

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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 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

Citations51
Published2006
Admission routes1
Has abstractyes

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