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Record W2113285883 · doi:10.5539/mas.v8n6p153

Correlation of Indoor Air Quality with Working Performance in Office Building

2014· article· en· W2113285883 on OpenAlexvenueno aff
Ismail Abdul Rahman, Jouvan Chandra Pratama Putra, Sasitharan Nagapan

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsnot available
FundersUniversiti Tun Hussein Onn Malaysia
KeywordsSick building syndromeOffice workersIndoor air qualityAir velocityAir conditioningVentilation (architecture)LethargyEnvironmental scienceIndoor airQuality (philosophy)Architectural engineeringProductivityAir quality indexAir temperatureOperations managementMeteorologyEnvironmental engineeringPsychologyEngineeringGeographyMechanical engineering

Abstract

fetched live from OpenAlex

In Malaysia, most of office building utilizes mechanical ventilation system to maintain its indoor air quality. However, if the mechanical ventilation system is not properly installed and maintained, it will contribute to poor indoor air quality which leads to decrease the productivity of office workers. This study assessed the correlation of indoor air quality toward working performance at office building in Universiti Tun Hussein Onn Malaysia using questionnaire survey. The findings revealed that the office can be categorized as sick building syndrome with the highest symptom is lethargy as marked by 75 % of the office workers. Since most of office workers are unauthorised to adjust temperature and air velocity of the air-conditioning system, this leads to the dissatisfaction toward indoor air quality where 40 % of the respondents are dissatisfied with temperature and air velocity. Ultimately, this study has successfully indicated that temperature has the strongest correlation with working performance as indicated by spearman correlation value of 0.648.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.276

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 studies0.0000.000
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 head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 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

Citations7
Published2014
Admission routes1
Has abstractyes

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