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Surface Delays for Gases Dispersing in the Atmosphere

2001· article· en· W2173332297 on OpenAlexafffund
John D. Wilson, Thomas K. Flesch, Réal D’Amours

Bibliographic record

VenueJournal of Applied Meteorology · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAtmosphere (unit)Reflection (computer programming)Standard deviationMean flowRoot mean squareBoundary layerDisplacement (psychology)MeteorologyGeologyParticle (ecology)TrajectoryMechanicsAtmospheric sciencesWind speedPlanetary boundary layerPhysicsGeodesyMathematicsTurbulenceStatistics

Abstract

fetched live from OpenAlex

When a particle descends beneath the (nominal) lower boundary of the atmosphere, it may remain there for some time τ before it reemerges into the (resolved) flow. In particle trajectory models, τ is the random duration of unresolved trajectory segments, below the height zr at which an artificial reflection boundary condition is applied. By computing such paths, for realistic near-ground flows, it was found that the mean delay per reflection is τ ≈ 2.5zr/σw where σw is the standard deviation of the vertical velocity at zr. The corresponding mean alongwind displacement per reflection, due to the mean horizontal wind u(z) below zr, is δ ≈ 〈u | zr〉 τ, where 〈u | zr〉 is the height average of u in the waiting layer. The fluctuating component of the horizontal wind causes no mean drift but upon each reflection contributes a random drift whose root-mean-square value is σδ ≈ 2zr. From simulations on the continental scale, with a lower boundary placed at zr ≈ 25 m, it was found that a typical particle suffered about 15 reflections per day, resulting in a net delay on the order of 30 min per day.

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 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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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
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

Citations6
Published2001
Admission routes2
Has abstractyes

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