Investigation on Non-Point Sources Approximation in Outdoor Noise Predictions
Bibliographic record
Abstract
ISO 9613 specifies a method for calculating the attenuation of outdoor sound propagation, which has been recognized and accepted by various regulatory authorities. As this method has been implemented in most advanced software packages, such as Cadna/A and SoundPLAN, this makes it feasible for predicting and resolving noise problems in a complex environment or the noise assessment of a large facility. However, an important decision to be taken is on how to model each noise source, in particular, deciding which situation the distribution of the individual component noise sources associated with a complex facility will affect the pattern of the noise field that emanates from it. ISO 9613 guides that line and area sources may be divided into sections, each represented by a point source at its center. However, this may only ensure the calculation accuracy at some distance from the single equivalent point source. Hence, a large segmentation of the sources is required to precisely calculate the noise level at the near field from line and area sources, which may increase calculation burden and may be only required in the near field. This paper will focus on comparing results between predicted and measured noise levels, overall and in octave-bands, which prove the validity of the proposed calculation method. Then, the validated model will be used to investigate the applicability and accuracy in the environmental noise prediction. The distance criteria for the point source method can be effectively applied for predicting and resolving noise problems in a complex environment for outdoor noise prediction and also for field measurement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".