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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 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

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.

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 categoriesnone
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 score0.205
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
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