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Record W2067038947 · doi:10.1080/02786820300912

Modeling Entry of Micron-Sized and Submicron-Sized Particles into the Indoor Environment

2003· article· en· W2067038947 on OpenAlexaff
Chwen Jyh Jeng, Warren B. Kindzierski, Daniel W. Smith

Bibliographic record

VenueAerosol Science and Technology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Stormwater Management Solutions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLaminar flowPenetration (warfare)Particle depositionMechanicsBrownian motionMaterials scienceGravitationParticle (ecology)Composite materialPhysicsClassical mechanicsTurbulenceGeologyMathematics

Abstract

fetched live from OpenAlex

A theoretical approach, based on particle dynamics, was used to examine the outdoor-to-indoor penetration coefficient ( P ) of fine particles inside thin rectangular cracks. Parallel-plate flow theory indicates that crack infiltration flow can be assumed laminar for long, thin rectangular cracks. Considering laminar crack flow, three particle penetration models were used to estimate P . They are the Licht model, the Fuchs model, and the Taulbee model. The first two models consider gravitational sedimentation as the particle deposition mechanism, while the third model considers particle deposition induced from both gravitational sedimentation and Brownian diffusion. Modeling results indicate that gravitational sedimentation governs particle deposition behavior for micron-sized particles, and that all three models can be used to model penetration for these particles. For submicron-sized particles, Brownian diffusion becomes the major deposition mechanism, and only the Taulbee model is suitable to model particle penetration. The Taulbee model was validated using published experimental results of other researchers. Model validation indicated that the Taulbee model satisfactorily estimates particle penetration for micron-sized and submicron-sized particles. Application of the three models to actual building penetration is discussed.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations14
Published2003
Admission routes1
Has abstractyes

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