Bibliographic record
Abstract
Sound fields in complex industrial workrooms can be predicted well using numerical procedures such as the method of images and ray tracing. However, this requires acoustical expertise, as well as computational resources and times which result in prediction methods only being used in special cases. This paper discusses alternative empirical prediction methods which have the potential to be sufficiently accurate in ‘‘typical’’ cases, and more readily accessible to practitioners, making them more likely to be used in practice. The first method discussed is a hybrid approach, whereby characteristic workroom sound-propagation curves are predicted using ray tracing. These are then input into an empirical model which sums the energy contribution of all sources at a receiver position based on those curves and the applicable source/receiver distances. Next, the development of empirical models for predicting frequency-varying sound-propagation curves and reverberation times using regression techniques is discussed. These were developed from data measured in actual workrooms when empty or fitted, without and with sound-absorptive treatment. Empirical methods for estimating workroom fitting densities and multisource noise levels, and the integration of the empirical models into the PlantNoise prediction system, are also discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".