Bibliographic record
Abstract
This paper uses a previously developed model of sound propagation in conventional open plan offices to explore the influence of each parameter of the office design on the expected speech privacy in the office.The ceiling absorption, the height of partial height panels and the workstation plan size are shown to be most important.However, it is not possible to achieve 'acceptable' speech privacy if all design parameters do not have near to optimum values.A successful open office should also include an optimum masking sound spectrum and an office etiquette that encourages talking at lower voice levels. SOMMAIRECet article s'appuie sur un modle de propagation du son dans les bureaux aires ouvertes mis au point antrieurement afin d 'analyser l'influence de chaque paramtre de la conception du bureau sur l'insonori sation du local en question.L'absorption du plafond, la hauteur des cloisons et les dimensions du poste de travail apparaissent tre les 3 paramtres les plus importants.Il est cependant impossible d'atteindre une insonorisation "acceptable" si tous les paramtres conceptuels ne sont pas proches de leurs valeurs opti males.Un bureau aires ouvertes russi doit aussi comprendre un spectre de son masquant optimal et une politique de bureau qui encourage parler voix basse.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".