Bibliographic record
Abstract
Barriers, such as walls, earth berms, and earth-berm/wall combinations, are used to control highway noise. Factors that affect noise attenuation include profile, height, location, and surface composition. A literature review was conducted on the topics of outdoor sound propagation, ground impedance, noise barriers, and acoustical scale modelling. The noise attenuation provided by vertical walls, earth berms, and earth berms crested by a vertical wall — for a line source of vehicular traffic — has received little attention. Using an ultrasonic air-jet source, scale-model materials were selected by measuring their Excess Attenuations, at scales of 1:20, 25, 31.5, 40, and 50. A new method was employed to select materials: the residuals between measurements and theoretical, best-fit curves were calculated for each cell of a 2-D array whose axes were scale factor versus effective flow resistivity. An optimal scale of 1:31.5 was selected, in conjunction with specifying three model materials to simulate berms and soft ground (expanded polystyrene), vertical walls (dense polystyrene), and roadways (varnished particle board). Berms, typically 4 m in height, were tested with slopes of 1.5, 2 and 3:1; for each of these slopes, five crest profiles were tested: wedge, flat-top (widths of 1 m and 2 m), and round-top (radii of 1 m and 2 m). For berms crested by a vertical wall, the relative heights of berm and wall were either lm/3m, 2m/2m, or 3m/lm; for each height combination, berm slopes of 1.5, 2, and 3:1 were tested. The surface composition of berms were altered using dense polystyrene, and felt. Weighted Insertion Losses (ILAs) for a line source were obtained by applying A-weighted, traffic-noise spectra, before integrating the 80-2500 Hz full-scale, thirdoctave bands. Contrary to current highway practice — where a "soft-top correction" of +3 dBA is often applied to earth berms — walls out-performed berms by 1-2 dBA. For berms, shallower slopes degraded third-octave ILs in the 250 Hz band, but had little effect on a berm's ILA. For berms crested by a vertical wall, shallower slopes increased ILAs.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".