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Record W2056864578 · doi:10.1002/clen.200800215

Effects of Renovation Work on Air Quality and Occupants Health in University Buildings

2009· article· en· W2056864578 on OpenAlexafffund
Asako Hasegawa, Hans Schleibinger, Gang Nong, Ewa Lusztyk

Bibliographic record

VenueCLEAN - Soil Air Water · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsIndoor air qualityEnvironmental scienceOdorVentilation (architecture)Work (physics)Indoor airArchitectural engineeringAir quality indexWaste managementEnvironmental engineeringEngineeringMeteorology

Abstract

fetched live from OpenAlex

Abstract This study aimed to prevent chemical air pollution and reduce its influence on occupants' health in a university building under renovation. Since starting the renovation work, the occupants' health status was monitored using questionnaires every summer and winter. According to a first questionnaire collected before the renovation, subjective symptoms were not observed. However, some occupants complained about odor after moving into their new rooms. Therefore, all interior materials used for the construction work were examined in small chambers to obtain their chemical emission characteristics. The assembled floor, which was a polyvinyl chloride floor sheet combined with a concrete slab by adhesive, was predicted to be the main source of odor in the renovated rooms. Two strategies were implemented individually in rooms to remove these odorous chemicals; a chemical filter in indoor air conditioning units and forced ventilation with hot‐humid outdoor air. The performance of each technique was validated by measuring the indoor concentration before and after operation. These results showed that both strategies had a significant effect on reducing volatile organic compounds (VOCs), in a practical way.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.013
GPT teacher head0.243
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2009
Admission routes2
Has abstractyes

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