Chapter 3. Raspberry Pi and Arduino Prototype: Measuring and Displaying Noise Levels to Enhance User Experience in an Academic Library
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it