The equivalent translational compliance of steel studs and resilient channel bars
Bibliographic record
Abstract
A number of recent papers have determined the compliance of steel studs for use in a model for predicting the sound insulation of cavity stud walls. Previously, the steel studs have usually been modelled as line connections. In this paper, they are also modelled as point connections where the points are the screws attaching the wall leaves to the steel studs. The compliance of the combination of resilient channel bars and wooden or steel studs, modelled as point or as line connections, is also presented. The values of compliance which make a theoretical model agree with the experimental data have been calculated. The experimental sound insulation data were measured by the National Research Council of Canada. There were 126 steel stud walls, 78 resilient channel bar and wooden stud walls, 15 resilient channel bar and steel stud walls and 4 walls with resilient channel bars on both sides of wooden studs. Linear regressions of the logarithm of the compliance against the logarithms of frequency, reduced surface density, cavity depth and number of point connections or stud spacing, were conducted in a low frequency range and in a high frequency range. The equations produced by these linear regressions can be used in sound insulation prediction models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".