Bibliographic record
Abstract
This paper discusses the development of empirical models for predicting total A-weighted speech levels and 1-kHz early-decay times in classrooms in an arbitrary state of occupancy. These are the two main quantities that affect speech intelligibility in classrooms. Three models for predicting early-decay time were developed. One was based on determining the contributions of various surface features to the average classroom-surface absorption coefficients. The other models, and those for predicting speech levels, were developed using multi-variable linear-regression techniques, and data previously measured in university classrooms or predicted empirically. By way of evaluation, the models were shown to re-predict the average values of the measured quantities in the original data-set with high accuracy, but they tended to underestimate the variability in the data. Predictions are presented to illustrate the performance of the models in the case of small and large hypothetical classrooms with low and high surface absorption, when unoccupied and occupied. The results are consistent with those measured in real classrooms. In particular, the speech-level model predicts physically-realistic decreases with distance from a speaker to a listener. The experimental data has also been used to determine typical ‘effective’ absorption coefficients for three classroom features – carpeted floors, absorbent ceilings and upholstered seating on carpeted floors – data which indicates the real-world performance that can be expected of these features, which may be useful in other prediction models and, for example, which provides information on the choice of treatments to meet the requirements of standards.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".