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Record W2051301972 · doi:10.1029/2007jf000791

A theory for drag partition over rough surfaces

2008· article· en· W2051301972 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsDragDrag coefficientParasitic dragZero-lift drag coefficientDrag equationSurface finishSurface roughnessDrag divergence Mach numberMechanicsLift-induced dragAerodynamic dragRoughness lengthPhysicsClassical mechanicsMaterials scienceMeteorologyThermodynamics

Abstract

fetched live from OpenAlex

We present a theory for drag partition over rough surfaces of arbitrary roughness density. The total drag is partitioned into a pressure drag, a ground‐surface drag, and a roughness‐element‐surface skin drag. The theory is simple but allows for the estimations of drag partition functions, friction velocity, zero‐displacement height, and roughness length. The model estimates of these quantities are compared with observations and the model is found to perform well. The theory explains several known facts from observations such as the dependency of aerodynamic roughness length on roughness density. It is shown that drag partition is governed entirely by two functions f r and f s which represent the dependencies of surface drag coefficient and the roughness drag coefficient on roughness density. Under the condition of f r = f s , the Raupach (1992) model is derived without assumptions in addition to the drag laws.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.436
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.042
GPT teacher head0.315
Teacher spread0.273 · 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