Chapter 3. Raspberry Pi and Arduino Prototype: Measuring and Displaying Noise Levels to Enhance User Experience in an Academic Library
Bibliographic record
Abstract
Chapter 3 of Library Technology Reports (vol. 54, no. 1), “Library Spaces and Smart Buildings: Technology, Metrics, and Iterative Design” Problems associated with noise in academic libraries are an ongoing concern for patrons and library administration. Noise disruptions come from numerous sources, including people, cell phones, audio players, and more. Chapter 3 of Library Technology Reports (vol. 54, no. 1), “Library Spaces and Smart Buildings: Technology, Metrics, and Iterative Design,” discusses how other researchers have previously collected data to measure noise levels in academic libraries; what steps they took to reduce noise, including staff monitoring, noise-level zoning, and reducing light levels; and the results of those studies. Janice Yu Chen Kung then shares how she and another librarian at Concordia University’s Webster Library in Montreal, Quebec, Canada, looked into solving noise disruptions at their library by providing real-time and quantitative data on noise levels to inform their users about the noise levels of different areas in the library, thus allowing users to choose the area in the library that best suited their needs. Kung discusses the technology used in their project, how they implemented the prototype, the challenges they encountered during the project, and the next steps.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".