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Record W2101701661 · doi:10.5194/bg-5-1259-2008

The impact of lateral carbon fluxes on the European carbon balance

2006· article· en· W2101701661 on OpenAlexaff
Philippe Ciais, Alberto Borges, Gwénaël Abril, Michel Meybeck, Gerd Folberth, Didier Hauglustaine, Ivan A. Janssens

Bibliographic record

VenueBiogeosciences · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCarbon sinkEnvironmental scienceCarbon fibersSink (geography)Carbon cycleEcosystemTotal organic carbonEstuaryOceanographyCarbon fluxAtmospheric sciencesClimate changeGeologyEcologyGeographyBiology

Abstract

fetched live from OpenAlex

Abstract. To date, little has been written about the important role played by processes transporting carbon laterally over continents, and from continents to oceans. These processes have an impact on the CO2 budgets and on the carbon budgets at local, regional and continental scales. We estimated the impact on the European carbon balance of the transport of carbon by the trade of wood and food products, by the emission and oxidation of reactive reduced carbon species, and by rivers and freshwater systems up to estuaries. The analysis is completed by new estimates of the carbon fluxes of coastal seas. The magnitude of the CO2 and carbon fluxes caused by lateral transport over Europe is comparable to current estimates of carbon gain by European ecosystems. At the continental level, we estimate a CO2 sink over Europe of 140 TgC yr−1 and a carbon sink of 50 TgC yr−1 being caused by lateral transport processes.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.196
Teacher spread0.190 · 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

Citations149
Published2006
Admission routes1
Has abstractyes

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