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Record W2153698307 · doi:10.1177/0040517507078041

Aerosol Filtration by Fibrous Filters: A Statistical Mechanics Approach

2007· article· en· W2153698307 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

VenueTextile Research Journal · 2007
Typearticle
Languageen
FieldEngineering
TopicAerosol Filtration and Electrostatic Precipitation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsAerosolFiltration (mathematics)FiberMonte Carlo methodStatistical mechanicsIsotropyParticle (ecology)MechanicsProcess (computing)Statistical physicsParticle filterIsing modelMaterials scienceBinary numberFilter (signal processing)Biological systemComputer scienceComposite materialPhysicsMathematicsMeteorologyOpticsStatistics

Abstract

fetched live from OpenAlex

A statistical mechanics approach, namely the Ising model combined with Monte Carlo simulation, was employed in studying the process of aerosol filtration through fibrous filters. This process was modeled as consisting of numerous cells' state exchanges driven by the difference of the system energy after and before a particle moved from one cell to the other and/or deposited on a fiber cell. With the use of a simpler binary algorithm, this approach was capable of realistically simulating the complicated mechanisms involved in the filtration process. Simulations were carried out for the behaviors of aerosol particles of different sizes interacting with isotropic fiber filters of various volume fractions. Simulation results were in good agreement with reported experimental data, indicating an encouraging prospect for the method to be applied in this area.

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)0.003
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: none
Teacher disagreement score0.967
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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