Quantifying the effect of flanking sound transmission on sound insulation and speech privacy metrics
Bibliographic record
Abstract
In modern commercial office design, sustainable building codes and quality standards often demand certain levels of sound insulation to ensure sufficient speech privacy or freedom from distraction for the occupants. Missing from the minimum requirements of these documents, however, is guidance on eliminating flanking weaknesses that repeatedly and significantly degrade the experienced speech privacy. This paper presents case studies of common acoustical weaknesses found between closed offices and meeting rooms. Through the use of sound intensity measurements and acoustical imagery, the sound power of each weakness is calculated and shown relative to the sound power of the separating partition. By comparing these results to partitions without these weaknesses, the effective reduction in speech privacy is demonstrated using the ASTC, NIC, and SPC metrics. Weaknesses examined and ranked include, open ceiling plenums, lack of or ineffective door seals, door closure pressure, continuous door frames, façade mullions and transoms, interior windows and their frames, lack of acoustical sealant, uninterrupted gypsum on side walls, common door frames at the edges of partitions, modular and operable walls and their junctions, continuous heating elements, thin continuous floor toppings, and ventilation duct crosstalk.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".