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Record W4298099009 · doi:10.5194/bgd-4-4067-2007

Quality control of CarboEurope flux data. Part 2: Inter-comparison of eddy-covariance software

2007· preprint· en· W4298099009 on OpenAlexaff
Matthias Mauder, Thomas Foken, R. Clement, J.A. Elbers, Werner Eugster, Thomas Grünwald, B.G. Heusinkveld, Olaf Kolle

Bibliographic record

VenueERef Bayreuth (University of Bayreuth) · 2007
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEddy covarianceSoftwareFlux (metallurgy)CovarianceQuality assuranceRange (aeronautics)Quality (philosophy)Computer scienceProcessingData processingInstrumentation (computer programming)Data miningRemote sensingEnvironmental scienceMathematicsStatisticsEngineeringPhysicsGeographyAerospace engineeringDatabaseChemistryOperations management

Abstract

fetched live from OpenAlex

Abstract. As part of the quality assurance and quality control activities within the CarboEurope-IP network, a comparison of eddy-covariance software was conducted. For four five-day datasets, CO2 flux estimates were calculated by seven commonly used software packages to assess the uncertainty of CO2 flux estimates due to differences in post-processing. The datasets originated from different sites representing different commonly applied instrumentation and different canopy structures to cover a wide range of realistic conditions. Data preparation, coordinate rotation and the implementation of the correction for high frequency spectral losses were identified as crucial processing steps leading to significant discrepancies in the CO2 flux results. The overall comparison indicated a good although not yet perfect agreement among the different software within 5–10% difference for 30-min CO2 flux values. Conceptually different ideas about the selection and application of processing steps were a main reason for the differences in the CO2 flux estimates observed. A balance should be aspired between scientific freedom on the one hand, in order to advance methodical issues, and standardisation of procedures on the other hand, in order to obtain comparable fluxes for multi-site synthesis studies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0030.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.265
Teacher spread0.228 · 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.

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

Citations18
Published2007
Admission routes1
Has abstractyes

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