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Record W2082130435 · doi:10.1029/2005jf000434

Aeolian sediment transport through large patches of roughness in the atmospheric inertial sublayer

2006· article· en· W2082130435 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 · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsUniversity of Guelph
FundersNational Aeronautics and Space Administration
KeywordsAeolian processesSediment transportGeologySurface finishSedimentGeomorphologyAtmospheric sciencesMaterials science

Abstract

fetched live from OpenAlex

Roughness influences the flux of wind‐driven sand transport. In this paper, we report on sediment transport measurements for four different surface roughness configurations composed of the same size (solid) roughness elements in the atmospheric inertial sublayer. Results of these tests indicate that sediment transport rates through patches of roughness in the atmospheric inertial sublayer are to a large extent controlled and scale proportionally with the roughness density (λ = n b h / S , where n is number of elements of breadth b and height h in area S ) of the surface. However, element size apparently increases the magnitude of the reduction beyond that attributable to λ. A sediment transport model that incorporates the effect of shear stress partitioning appears to predict reasonably well the effect of roughness on sand transport in the cases where the roughness elements are ≤0.10 m in height. However, when the dimensions of the roughness itself are equivalent to or are greater than the range of saltation lengths (vertical and horizontal), additional interactions of the elements with the saltation cloud appear to reduce the transport efficiency.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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