Bibliographic record
Abstract
In open-plan offices speech is often the most distracting cause of noise even though a main aim of open-office design is to attenuate speech propagating between workstations so that it does not disturb the workers’ concentration (Larm et al., 2005). In order to achieve adequate speech privacy in open offices, appropriately designed noise-masking systems are often used (Hongisto, 2008). Noise-masking systems consist of loudspeakers located behind the suspended acoustical ceiling distributed throughout the office area; they generate a background noise over a certain area, to mask the unwanted sounds. That should result in increased speech privacy, eliminating the imposition of unwanted sound, and enhancing the general level of acoustical comfort and productivity in the space. This pilot study was done to evaluate the advantages and the disadvantages of the noise-masking system recently installed on one of the two floors in the Vancouver Coastal Health Community Health-Care facility (CHC). In order to achieve this general goal it was important to determine the effects of this noise-masking system on background-noise levels, speech privacy, and on the workers ’ performance.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.182 | 0.050 |
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".