The Effect of Office Design on Workstation Lighting: A Simulation Study
Bibliographic record
Abstract
It has long been recognised that the lit environment in open-plan office space is influenced by the density and properties of the installed furniture. Indeed, the Lumen Method includes a procedure involving look-up tables to account for the effects of workstation size, partition height, and partition reflectance on the mean working-plane illuminance. As part of a larger project on open-plan office environments, we used the Lightscape TM simulation tool to further explore the effect of office design on the lit environment in workstations. The office design variables of interest were workstation size, partition height, workstation reflectance, ceiling reflectance, and ceiling height. In addition to desktop illuminance, our outcome variables included illuminance distribution and partition luminance. We performed simulations for combinations of these parameters for fourteen common lighting designs for North American open-plan office space, including direct prismatic and parabolic luminaires, and indirect and direct/indirect luminaires. The results are expressed in simple linear or curvilinear relationships between office design variables and luminous variables. Results follow expected trends, and are consistent with previously published work in this area.Furthermore, in going beyond mean desktop illuminance, our results extend knowledge of therelationships between office design variables and the luminous environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".