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Record W2303599643 · doi:10.5194/gmd-9-3321-2016

A new stepwise carbon cycle data assimilation system using multiple data streams to constrain the simulated land surface carbon cycle

2016· article· en· W2303599643 on OpenAlexfundno aff
Philippe Peylin, Cédric Bacour, Natasha MacBean, Sébastien Léonard, P. J. Rayner, Sylvain Kuppel, Ernest N. Koffi, Abdou Kane, Fabienne Maignan, Frédéric Chevallier, Philippe Ciais, Pascal Prunet

Bibliographic record

VenueGeoscientific model development · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
FundersFP7 SpaceLawrence Berkeley National LaboratoryCanadian Foundation for Climate and Atmospheric SciencesOak Ridge National LaboratoryBiological and Environmental ResearchNatural Sciences and Engineering Research Council of CanadaMicrosoft ResearchNatural Resources CanadaUniversité LavalSeventh Framework ProgrammeU.S. Department of EnergyNational Science Foundation
KeywordsEnvironmental scienceData assimilationCarbon cycleBiogeochemical cycleModerate-resolution imaging spectroradiometerNormalized Difference Vegetation IndexBorealClimatologyTerrestrial ecosystemGreenhouse gasAtmospheric sciencesRemote sensingEcosystemLeaf area indexSatelliteMeteorologyEcologyGeology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.237
Teacher spread0.198 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations125
Published2016
Admission routes1
Has abstractyes

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