Heavy and light impact sources to rate impact sound transmission and changes due to applied floor toppings
Bibliographic record
Abstract
The magnitude and spectrum of the power injected by an impact source depends on the impedance of the source and floor. If standardized impact tests are meant to give ratings that correlate well with subjective impressions of footstep noise, it follows that the impact source used should have the same impedance as an average human foot at least over the range of test frequencies. The ISO tapping machine, the Japanese tire machine, and an 18-cm-diam rubber ball do not satisfy this criterion. Consequently, their impact spectra differ from those from a live walker. Floor toppings, in particular, are ranked differently. Examples of discrepancies will be presented for direct transmission between vertically separated rooms. For horizontally and diagonally separated rooms, flanking transmission controls the impact sound pressure level. The sound pressure level depends not only on the power injected by the source, but also source location relative to the flanking junction because of propagation attenuation across the floor. Sensitivity to source location is similar for different sources (ISO tapping machine and Japanese ball), suggesting that the same source could be used for direct and flanking transmission measurements if the source adequately simulates the impedance of a human foot.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".