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Record W3021114849

Sheltering in buildings from large-scale outdoor releases

2004· article· en· W3021114849 on OpenAlexaboutno aff
Wanyu R. Chan, Phillip N. Price, Ashok Gadgil

Bibliographic record

VenueeScholarship (California Digital Library) · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsnot available
Fundersnot available
KeywordsToxic gasHarmEnvironmental scienceDoorsSignageVentilation (architecture)AccidentalPreparednessScale (ratio)BusinessEnvironmental planningEnvironmental engineeringEngineeringGeographyMeteorologyPolitical scienceAdvertising
DOInot available

Abstract

fetched live from OpenAlex

Air Infiltration and Ventilation Centre Ventilation Information Paper Sheltering in Buildings from Large-Scale Outdoor Releases W.R. Chan, P.N. Price, A.J. Gadgil 1. Introduction Intentional or accidental large-scale airborne toxic release (e.g. terrorist attacks or industrial accidents) can cause severe harm to nearby communities. Under these circumstances, taking shelter in buildings can be an effective emergency response strategy. Some examples where shelter-in-place was successful at preventing injuries and casualties have been documented [1, 2]. As public education and preparedness are vital to ensure the success of an emergency response, many agencies have prepared documents advising the public on what to do during and after sheltering [3, 4, 5]. In this document, we will focus on the role buildings play in providing protection to occupants. 2. How effective is sheltering? The sudden nature of a terrorist or accidental release means that there is often not enough time to safely evacuate the nearby communities. The remaining option is to take shelter until the toxic plume has dispersed. The obvious advantage of staying indoors is that there is a reservoir of clean air contained in buildings. Even though buildings are not airtight, building envelopes restrict the transport of the toxic pollutant to the indoors. The result is that the indoor concentration will increase much slower and remain low relative to the outdoor concentration. 2.1 Outdoor-indoor air exchange When sheltering in buildings, doors and windows should be closed, and ventilation and exhaust fans should be off to minimize air exchange with the outdoors. In such cases, the air change per hour (ACH) is determined by uncontrolled air leakage across the building envelope (Figure 1). Air infiltration is a function of the leakiness of the building, and the differential pressures across the envelope, which are caused by indoor-outdoor temperature difference and the forces exerted by wind. Air infiltration rates can vary from less than 0.1 ACH for a tight house under mild weather conditions to over 1.5 ACH for a leaky house under severe weather conditions (Table 1). These values are derived from air leakage measurements of residential houses in the US [7]. Houses in countries where the climate is more severe, such as Sweden, Norway, and Canada, tend to be more airtight than the values presented here [8].

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.079

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.004

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 head0.195
Teacher spread0.187 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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