Global understanding of important parameters for improvement of impact insulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Floor impact insulation performances can be very different from one assembly to another. Many years of research and development have been done in this area. Now, it seems that a new solution or another product is commercialized every week. From buyers' point of view, there is a need to decide which topping and underlay will suit some noise requirement to the lowest cost. However, acousticians and specialists in noise control might consider a more complex problem, especially in multi-family dwellings. In lightweight construction, the relation between the floor and the ceiling underneath also affects the overall performance in terms of IIC, or even in risk of complaints. Knowing that it is often difficult to compare a real situation to a datasheet from a manufacturer or to building codes, few key ideas should be remembered. This paper aims to briefly review some conclusions of previous works done in impact sound insulation and to analyze how fundamental parameters can be applied to real installations. An example of variable modification on a topping sample also tries to demonstrate the influence of basic aspects without according most attention to single number ratings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it