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Record W2221409531 · doi:10.1177/1744259115603041

Is there an optimum range of airtightness for a building?

2015· article· en· W2221409531 on OpenAlexaff
Mark Bomberg, Tomasz Kisilewicz, Katarzyna Nowak

Bibliographic record

VenueJournal of Building Physics · 2015
Typearticle
Languageen
FieldEngineering
TopicHygrothermal properties of building materials
Canadian institutionsMcMaster University
FundersLawrence Berkeley National Laboratory
KeywordsEnclosureAirflowEnvironmental scienceMoistureAir movementDurabilityArchitectural engineeringVentilation (architecture)Environmental engineeringEngineeringWaste managementMaterials scienceMechanical engineeringComposite materialMeteorology

Abstract

fetched live from OpenAlex

Air transport control has been recognized as critical to the proper functioning of buildings. Airflow is related to all facets of environmental control because it influences transport of heat and moisture and affects indoor environment as well as the durability of the building enclosure. To a lesser degree, we also recognize that contamination of wall cavities in building assemblies by organic materials from inside or outside provides both the nutrients and the inoculation potential for mold growth. Moisture carried by air may also increase the rate of emission of volatile organic compounds from these materials. While keeping rain out of building enclosures is a primary consideration in design, controlling airflow through the building enclosure comes a close second in importance to allow environmental control within buildings. Yet, an increase in the airtightness comes with a cost as well as an increased risk of moisture entrapment in case of any failure, and this, in turn, relates to the type of the building.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.048
GPT teacher head0.282
Teacher spread0.234 · 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

Citations31
Published2015
Admission routes1
Has abstractyes

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