Optimization of the acoustic treatments in order to achieve the Italian minimum environmental criteria (CAM) for classrooms
Bibliographic record
Abstract
A recent Italian legislation has established the "Minimum Environmental Criteria" (CAM) for working environments. With regard to schools, adequate acoustic comfort targets are required in terms of noise control and acoustic quality. Schools must comply with the UNI 11532 for their reverberation (T), clarity (C50) and speech intelligibility (STI). In classrooms, the following values are required: T<0.7s, C50>0dB and STI>0.6. In sports facilities such as gyms or swimming pools, the requirements are T<1.5 s, C50>-2 dB and STI>0.5. To achieve these objectives, the insertion of acoustic treatments is often unavoidable. Acousticians commonly use the perfectly diffused theory to calculate the amount of need sound-absorbing material to comply with current legislation. The purpose of this work is to evaluate the optimal position in which to place the minimum amount of sound-absorbing treatments to reach CAM. Some university classrooms are used. These classrooms have a marble floor and plastered walls and, at 1.0 kHz, have T values between 2.5s and 4.5s, C50 between 3 dB and -0.5 dB, and ST between 0.34 and 0.47. Using an acoustic software, it was possible to estimate the minimum quantity and the optimal placement of the sound-absorbing panels to insert to reach the CAM of these classrooms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".