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Record W2154750738 · doi:10.1029/2007jf000780

Humidity control of particle emissions in aeolian systems

2008· article· en· W2154750738 on OpenAlexaff
Cheryl McKenna Neuman, Steven Sanderson

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsTrent University
Fundersnot available
KeywordsHumidityWind speedParticle (ecology)Wind tunnelRelative humidityKelvin equationAeolian processesAerosolMaterials scienceEnvironmental scienceSurface finishSurface roughnessAirflowMeteorologyMechanicsAdsorptionComposite materialThermodynamicsPhysicsChemistryGeology

Abstract

fetched live from OpenAlex

Humidity is an important control of the wind speed required to entrain particles into an air flow and is well known to vary on a global scale, as do dust emissions. This paper reports on wind tunnel experiments which quantify this control through placing a polymer capacitance sensor immediately at the bed surface. The sensor measured changes in the humidity (RH) of the pore air in real time. RH was varied between 15% and 80% and the critical wind speed determined for the release of particles to the air stream. The results strongly support earlier suggestions that fine particles are most affected in relatively dry atmospheres, particularly those which are tightly packed. An analytical model is proposed to describe this relationship which depends on determination of the matric potential from the Kelvin equation. The total contact area between particle asperities adjoined by pendular rings is represented as a power function of the number of layers of adsorbed water. The value of the exponent appears to be governed by the surface roughness of the particles and their packing arrangement. Parallel developments in colloid interface science and atomic force microscopy, relevant to industrial and pharmaceutical applications, support these conclusions in principle and will likely have an important bearing on future progress in parameterization of the proposed model.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 head0.044
GPT teacher head0.306
Teacher spread0.261 · 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 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

Citations76
Published2008
Admission routes1
Has abstractyes

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