Sound Ecology and Acoustic Health, Part 2: An Android Application for Recording Noise Nuisances
Bibliographic record
Abstract
Introduction to periodical: In our last month’s article, CC Issue 300, we light-heartily discussed a supposed back yard BBQ discussion between neighbours about urban noise nuisances. Unfortunately noise nuisances are real in some of our local Calgary Communities, and we are looking for some simple, inexpensive approaches to help people investigate and reduce the problem. We demonstrated the first steps of our solution - the development of an Android project with basic code to generate a main screen with a button that generated a welcome screen when pressed. We called this a WAT_AN_APP, meaning we were able to develop it Without Any Teenager Assistance being Necessary. In this article we want to extend our basic WAT_AN_APP project to recording and playing-back audio .3GPP files as shown in Fig. 1A. This will allow us to record any physical noises present that are less easily heard by others in your house or need more study as they are less noticeable during the day as they are hidden under traffic noise. In this article, we want to take a more adult approach – use a JEAC process that uses Just Enough Additional Code to make the new recording activity work.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.046 | 0.024 |
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".