Tonal noise in buildings: Current practice in measurement and assessment
Bibliographic record
Abstract
A review of the research and guidelines for tones that may be applied towards buildings is discussed. A tone is defined as a sound where most of the energy is concentrated at a single frequency, and is more likely to be detected at low levels. Tones are rarely found in areas without considerable background noise or masking, which was further found to be dependant on both the level of the tone over the background noise, and the level of the background noise itself. Kryter and Pearsons developed a methodology based on the judgment of random train noise containing a pure tone, resulting in the Tone Corrected Perceived Noise Level. The Federal Aviation Regulation (FAR) 36, Section A36.4 outlines the process known as the Effective Perceived Noise Level (EPNL), which is a single number evaluator of the subjective effects of noise. An indoor setting also is dependent on many non-acoustical factors, including room aesthetics and occupant occupation.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".