Determination of sound pressure levels <i>in</i> <i>situ</i> using sound intensity measurements
Bibliographic record
Abstract
Measurements of emission sound pressure levels of machinery require either specially defined test rooms or calculated corrections for the acoustic environment. In principle, it is possible to determine emission sound pressure levels from sound intensity measurements at specified work stations in any test environment if the requirements of background noise levels and field indicators are fulfilled. The draft international standard ISO 11205/CD specifies such a method. In this paper the accuracy of emission sound pressure levels using sound intensity measurements was examined for three small sources in three acoustic environments, an anechoic environment with loudspeakers to simulate background noise, an office environment, and a reverberant environment inside a stairwell. In the first two environments good measurement accuracies, within 1 dB, were obtained. Sound intensity measurements by pointing the probe towards the source were as accurate, and simpler than computation of the resultant intensity using three arbitrary orthogonal measurements. As predicted by field indicators, measurements in the stairwell gave unacceptable errors for all three sources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".