Scale model investigation of factors affecting the performance of roadside noise barriers.
Bibliographic record
Abstract
Besides numerical models, another way to model complex environments is using physical reduced-scale models. This project used scale models to study several factors which may influence the performance of roadside noise barriers. One is the barrier absorption. Making the barrier sound absorptive decreases reflections and may decrease amplification between parallel barriers. The other is tree foliage growing near the barrier. Tree foliage may scatter sound into the shadow zone behind the barrier, increasing noise levels and decreasing the insertion loss. Tree foliage may also attenuate sound that would normally be diffracted into the shadow zone, actually increasing the insertion loss. A 1:31.5 scale model was created in an anechoic chamber to test the effects of these two factors. Excess attenuation measurements were performed to choose scale model materials, which accurately represent full scale surfaces. Parallel barriers were modeled with a reflective surface and their IL's were measured for different configurations of absorptive covering. IL's of single barriers were measured both with and without scale model trees placed either in front or behind them. The results are compared to previously performed field test measurements.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".