Error in predicted flanking sound reduction index due to reduction of average area in velocity measurements
Bibliographic record
Abstract
In a recent research project equations for the prediction of flanking sound transmission through lightweight framed assemblies are derived in a statistical energy analysis (SEA) framework and prediction results are validated with well established measurement method. The equations of the new method are similar to those of the EN 12354 method for heavy homogenous monolithic structures that are weakly damped and support a diffuse structure-borne wave field. However, good prediction results are obtained with the new method only if measured velocity level differences are used as input data because propagation of bending waves is strongly attenuated in lightweight framed structures due to the fairly high loss factor of the structure in combination with the rather short bending wavelength of the leaves. The great velocity gradient on the receive element leads to the assumption that most sound power is radiated by the area of high velocity close to the junction into the receive room. Good prediction results are also obtained when the area that is considered on the receive leaf for the velocity level measurement is reduced moderately and the element area in the prediction is adjusted accordingly. The change in flanking sound reduction index due to this area reduction is investigated more thoroughly in this paper and a simple model of a plane propagating banding wave on a damped infinite plate is used to estimate the error in the prediction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".