On the importance of the direct field in structure borne transmission in framed constructions
Bibliographic record
Abstract
This paper reports findings from a recently completed study of sound transmission in wood framed buildings. The paper begins by showing that the impact sound insulation of floor flanking paths is strongly dependent on the distance from the flanking junction and that adding a topping to the floor modifies the sensitivity to source location. Vibration draw-away measures on the exposed floor surface are used to demonstrate the attenuation of structure borne vibration is different in the directions parallel and perpendicular to the joists, and the attenuation with distance can be significantly modified by the presence of a topping. These vibration measures and the change in Flanking-NISPL with source position indicate that diffuse field assumptions are not valid and suggest that some form of a direct field controls the incident structure borne power. It is also shown that vibration draw-away measures (made on the exposed surface of the floor) can be used to accurately predict the change in Flanking-NISPL with source location. These data are used to explain why the effectiveness of floor toppings to control floor-flanking paths is a function of joist orientation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".