Assessing community noise annoyance: A review of two decades of the international technical specification ISO/TS 15666:2003
Bibliographic record
Abstract
The robust assessment of noise annoyance is of key importance given that it is the most prevalent community response in populations exposed to environmental noise. In 1993, the International Commission on Biological Effects of Noise Community Response to Noise team began formalizing a standardized methodology for assessing noise annoyance which resulted in reporting guidelines and recommendations later published as a Technical Specification (TS) in 2003 by the International Standards Organization (ISO) [(2003). ISO/TS 15666]. This TS, intended to inform the international community on the quantification of the exposure-response relationship between noise exposure and annoyance, has been in circulation for nearly two decades and was updated in 2021 by ISO [(2021). ISO/TS 15666] by an international working group (ISO TC43/SC1/WG62). This paper reviews use of the 2003 TS, identifies common adaptations in use, and summarizes the revisions. Methodological issues arising from the use of the 5-point verbal and the 11-point numeric scale questions and the scoring of "highly annoyed" are discussed. The revisions are designed to encourage further standardization in noise annoyance research. The paper highlights research needs that, if addressed, would strengthen the methodology underlying the assessment of noise annoyance including multidimensional assessments of annoyance.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".