Post Occupancy Evaluation towards Indoor Environment Improvement in Malaysia’s Office Buildings
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.
Bibliographic record
Abstract
The indoor environmental factors considered in office building must be determined in order to meet user’s requirement. Disruption of indoor environment may constitute to reduce occupants’ efficiency and work productivity. Therefore, Post-occupancy Evaluation (POE) is a prominent tool that able to indicate satisfaction and comfort level needs by building occupants as lessons learned to identify problems in indoor environment. By using occupants as benchmark of evaluation, the potential of improving the indoor environment in buildings is enormous. With relation to the title, the main purpose of this study is to provide recommendation to improve quality of indoor environment in office building, by analytical study on the level of effort and phases of conducting POE and determining occupant’s satisfaction level. The survey used in conjunction with physical measurements to determine how environmental factors affect occupants’ perceived comfort and productivity levels, and a benchmarking example of using the survey to establish how new buildings are meeting a client’s design objectives. It is recommended that the application of POE is prioritized as strategic level decision making and emphasized as continuous activity in environmental evaluation.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it