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Record W2026539639 · doi:10.1002/joc.652

Comparison of two‐layer and single‐layer canopy models with Lagrangian and <i>K</i>‐theory approaches in modelling evaporation from forests

2001· article· en· W2026539639 on OpenAlexaffabout
Alex Wu, T. Andrew Black, Diana Verseghy, W. G. Bailey

Bibliographic record

VenueInternational Journal of Climatology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsCanopySensible heatUnderstoryEnvironmental scienceAtmospheric sciencesLatent heatTree canopyStomatal conductanceLeaf area indexFlux (metallurgy)LagrangianMeteorologyMathematicsGeographyEcologyGeologyBotanyMaterials scienceBiology

Abstract

fetched live from OpenAlex

Abstract The near‐field effect on flux calculation was examined using two approaches: (1) The performance of a Lagrangian two‐layer canopy model was compared with a K ‐theory two‐layer canopy model and a K ‐theory single‐layer canopy model; and (2) the near‐field resistance was placed in series with aerodynamic resistance in the Canadian Land Surface Scheme (CLASS). The first approach was tested using flux data measured from a boreal aspen forest because it had a thick understorey canopy. The second approach was tested using the aspen forest and a Douglas fir forest. Results from both approaches confirmed that the difference between simulations from K ‐theory and the Lagrangian evaporation models was small due to the strong control by stomatal conductance. Furthermore, the two‐layer canopy models were inferior to the single canopy model in the calculation of the sensible and latent heat fluxes above the forest. Copyright © 2001 Royal Meteorological Society

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.242

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 studies0.0000.000
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 head0.060
GPT teacher head0.281
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations35
Published2001
Admission routes2
Has abstractyes

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