On using ordinary mobilities to predict structure borne power flow from studs to directly attached gypsum board of a wood stud wall—Effect of simplifying assumptions
Bibliographic record
Abstract
This paper reports on the effect of simplifying assumptions made when applying ordinary point mobilities (from thin plate/beam theory) to predict structure borne power, and hence velocity level difference (VLD), from a stud to gypsum board attached by screws. The systematic study showed for practical purposes the fastening points of a gypsum board sheet were sufficiently far from an edge so that the measured mobilities approached those of a drive point located near the sheet center. The mobility of a stud does not change appreciably when attached to the wall head and sole plates. Above the transition frequency from line to point connection where fastener spacing is greater than one half wavelength VLD is inversely proportional to the number of fasteners. Measurements indicate above 1000 Hz local and volumetric deformation of the stud are important causing a very significant VLD across the stud. Deep beam theory, which includes area of the contact between the stud and gypsum board must be included. Between 125–1000 Hz, where the basic assumptions are reasonably satisfied, there is very good agreement between measured VLD and predicted using thin plate/beam mobilities. Predictions indicate agreement above 1000 Hz is greatly improved using deep beam theory.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".