Measurement of flanking transmission through gypsum board walls with a modified SEA method
Bibliographic record
Abstract
This paper reports results from a research project that examines flanking sound transmission involving lightweight gypsum board walls using SEA. There are multiple transmission paths between any two gypsum board surfaces coupled by the junction. This is because the gypsum board leaves of a wall are often resiliently mounted to the studs, which forms a cavity and transverse velocity is not the same on either side of the wall. Often the assumption is made that power flow occurs along every path independently and the power balance method is applied to estimate the flanking transmission along every path independently. This approach overestimates the power flow along each of the single paths, since their magnitude is a complex function of the wall structure. However, two different approaches of application of the power balance method to estimate the total flanking sound transmission between two rooms from measured surface velocity level differences between two leaves are presented in this paper. One is a common approach that was already applied in earlier work to identify the relative importance of the different paths that might contribute to the overall transmission at the regarded junction. The velocity level difference is measured between the surfaces of the flanking walls that are exposed two the source and receiving room respectively where the first is excited structurally. In the second, the modified measurement approach, the surface velocity of the wall that is not exposed to the source room is measured instead to get some benefit in predicting the structural response of this leaf to airborne excitation in the source room. The results of the methods are compared and the advantages as well as the disadvantages of both methods are discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".