Empirical Prediction of Workshop Fitting Densities for Noise Prediction by Ray Tracing
Bibliographic record
Abstract
Empirical models were developed for predicting frequency-varying fitting densities in industrial workshops for use in the prediction of noise levels by a ray-tracing model. Eleven typical workshops with varying dimensions, types, quantities and distributions of fittings, in which octave-band sound-propagation curves and the fitting dimensions had been measured, were involved. The workshops were modeled and sound-propagation curves were predicted for a range of fitting densities. The predicted curves were compared with the measured curves to determine the ‘best-fit’ fitting density. Linear-regression analysis was then used to find empirical models for predicting the best-fit fitting densities from physical parameters calculated from the fitting and workshop dimensions. The average fitting-to-workshop height ratio, the fitting-to-workshop volume ratio and the number of fittings were the parameters that predicted the fitting density best. Preliminary validation work, involving the comparison of sound-propagation curves predicted with the empirically-predicted fitting densities by ray tracing and the curves measured in four other workshops, suggests that the empirical models are inherently valid.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".