Bibliographic record
Abstract
An investigation was made of the acoustical environments in the Berwick Preschool, Vancouver, in response to complaints by the teachers. Reverberation times (RT), background noise levels (BNL), and in-class sound levels (Leq) were measured for acoustical evaluation in the classrooms. With respect to the measured RT and BNL, none of the classrooms in the preschool were acceptable according to the criteria relevant to this study. A questionnaire was administered to the teachers to assess their subjective responses to the acoustical and nonacoustical environments of the classrooms. Teachers agreed that the nonacoustical environments in the classrooms were fair, but that the acoustical environments had problems. Eight different classroom configurations were simulated to improve the acoustical environments, using the CATT room acoustical simulation program. When the surface absorption was increased, both the RT and speech levels decreased. RASTI was dependent on the volumes of the classrooms when the background noise levels were high; however, it depended on the total absorption of the classrooms when the background noise levels were low. Ceiling heights are critical as well. It is recommended that decreasing the volume of the classrooms is effective. Sound absorptive materials should be added to the walls or ceiling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".