Optimizing Ceiling Systems and Lightweight Plenum Barriers to Achieve Ceiling Attenuation Class (CAC) Ratings of 40, 45 and 50
Bibliographic record
Abstract
Acoustics is one of the lowest scoring indoor environmental quality metrics in building occupant surveys. This is in part due to the misconception that a modular acoustic ceiling alone can be used to block noise when a room's demising walls do not extend full height. Acoustic codes, standards and guidelines typically require 40, 45 or 50 decibels (dB) of isolation between rooms, yet most ceiling panels only provide 20-35 dB of inter-room blocking. Penetrations for lights, grilles and diffusers, can decrease Ceiling Attenuation Class (CAC) by 10 points overall and 20 dB in the 1,000, 2,000 and 4,000 Hertz (Hz) octave bands. A full-height wall is the preferred way to block inter-room noise transfer, but when combined with the 20-25 dB of blocking provided by typical ceiling systems that have been penetrated by lighting and air distribution devices, it can result in unnecessary costs to the project. The isolation provided by the ceiling and upper wall exceeds that provided by the lower wall. Laboratory tests were conducted to optimize combinations of modular acoustic ceilings and lightweight, top-of-wall, plenum barriers that result in CAC ratings of 40, 45 and 50 points. The ceiling systems and plenum barriers contained multiple penetrations for building services, representing real-world applications. This research is a continuation of the research presented at Acoustics Week in Canada 2015 in Halifax.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".