Impact sound measurements on floors covered with small patches of resilient materials or floating assemblies
Bibliographic record
Abstract
Resilient materials are commonly placed on top of a hard floor surface to reduce impact sound transmission to rooms below. Examples of such are carpets and vinyl or cork flooring. A raft of material, such as a wood or concrete slab, may also be placed on top of a resilient layer to form a "floating" floor. All of these systems may be referred to collectively as floor toppings. The same floor topping in combination with different base floors provides quite different impact sound insulation ratings, partly because the base floors give different sound insulation and partly because of different interactions with the base floor. Thus it is often impossible to select the most effective floor topping from several that have been tested on different base floor assemblies and only composite impact sound insulation ratings are available. An ISO test procedure measures the improvement due to a floor topping when it is placed on a concrete slab. The improvement may then be used to estimate the impact sound insulation of floors incorporating concrete slabs. This project confirmed that the ISO procedure works well and that small areas of floor topping specimens can be evaluated without serious error. The measurements showed that these improvements may not be applied to joist floors with lightweight subfloors such as plywood. Improvement ratings for a number of generic materials are provided in the report.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".