Teaching Concepts of Acoustical Waves in Air - Part 2
Bibliographic record
Abstract
This paper has been written to build on Part 1 of this series which was presented at the 2022 Acoustics week in Canada and similar to Part 1 contains materials extracted from 30 years of teaching to Architects at the University of Waterloo and Dalhousie University in Halifax. The purpose of this session is to provide teachers with practical demonstrations which can be used to enhance learning. I have always had a “hands on” teaching philosophy that a teacher engage multiple senses to be effective at instilling knowledge, and what is better for architects and acousticians than using sight and sound as well as written materials. Part 1 dealt primarily with sound propagation in air and the concepts of longitudinal wave motion, speed, frequency and wavelength and related effects which relate to what we perceive as pitch. Part 2 expands on those concepts by discussing superposition and introducing the definition and measurement of sound pressure, decibels and the decibel scale and how to manage decibels, all of which we perceive as loudness. If time permits, we will examine the hearing mechanism to discern how we physically perceive pitch and loudness.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".