A method for prioritizing the modification of ergonomic and physical aspects of the workplace to enhance overall worker satisfaction in control centre buildings
Bibliographic record
Abstract
This article aims to develop a method for prioritizing indoor environmental quality parameters in the workplace (i.e., temperature, lighting, acoustics, air quality, layout, furnishing, cleanliness and maintenance) to enhance occupants' workspace satisfaction. Data were collected using a web-based survey of 12 Iranian control centre buildings (CCBs) of combined cycle power plants. The results showed that fewer than half of occupants are satisfied with their workplace. Corrective measures would cost the owners an exorbitant amount of money if they were to try to address all of the parameters. Therefore, a statistical analysis framework was applied to determine each parameter's importance in relation to overall workspace satisfaction. Based on detailed analysis, two levels of importance have been defined for ergonomic modification of each CCB. The statistical approach developed in this study can be applied to all kinds of buildings to determine where ergonomic modification is most likely to produce higher workspace satisfaction.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".