Empirical Prediction of Workshop Fitting Densities for Noise Prediction by Ray Tracing
Why this work is in the frame
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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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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 it