Bibliographic record
Abstract
Noise exposure represents a major environmental issue and a burden on public health. The measurement and analysis of acoustic parameters through short-term and long-term noise monitoring is required for the identification of excessively noisy areas and planning for corresponding noise abatement measures or soundscape interventions. Major public health organizations such as the World Health Organization set up guidelines on maximum average noise exposure based on acoustic indicators such as Lden and Lnight. We present noisemonitor (https://pypi.org/project/noisemonitor/), a python package for short-term and long-term sound level monitor data analysis. The package allows for the calculation of acoustic indicators including average LAeq, Lden, LA10 or LA90 from short or long-term sound level monitor data in a few lines of code. In addition, it allows to compute and plot weekly and daily rolling averages to observe daily profiles and easily identify trends such as weekly public space use or recurring noise emissions. This python package could save time for professionals of the built environment by providing an easy-to-use tool for sound level monitor data analysis.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".