Bibliographic record
Abstract
This paper will review recent work that has considered speech privacy criteria in open-plan offices and the development of new procedures to assess the architectural speech security of closed offices and meeting rooms. For open-plan offices, new subjective studies have confirmed the validity of a speech privacy design goal of SII no more than 0.20 (AI 0.15) and that an ideal masking noise level is ∼45 dBA. For the speech security of closed meeting rooms, subjective studies have developed new weighted signal-to-noise ratio measures for rating the privacy of meeting rooms and a new measurement procedure has been developed to assess conditions close to the outside boundaries of a meeting room. While much progress has been made, further work is still required. For example, it is now clear that in many situations, privacy is significantly influenced by both signal-to-noise and reverberation in the rooms. Both factors must be included when assessing the privacy of situations with significant amounts of reverberation.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".