Bibliographic record
Abstract
In a significant government building a living wall was installed as part of a major renovation. The wall is two storeys high and the upper storey opens up to two large meeting rooms. When it used for meetings the noise from the irrigation system of the living wall interferes with the speech communication of attendees. The rooms have extensive glazing and exposed natural stone walls. The ceiling is of irregular shape with acoustical treatment and the floor is carpeted. The challenge is to devise a solution that will address the noise problem as well as ensure continued health of the plants as well as the operation of the rooms and esthetics. Several measurements were performed as well as acoustical modelling to explore possible solutions. The other factor to consider in modelling is that the wall does not act as a point source but rather a series of line sources. The results of the modelling and how the measured data fits will be presented. The noise is primarily related to the flow rate of the irrigation system. An empirical derivation of noise with flow rate is about 50*log10(flow rate). The results provide one with understanding of the possible challenges of such installations and the design considerations that can help alleviate noise issues after the wall is operating. https://awc.caa-aca.ca/index.php/AWC/awc17/author/saveSubmit/3
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".