A review of port noise management strategies to improve sonic cohabitation
Bibliographic record
Abstract
Sonic cohabitation between port areas and the communities living nearby is a major concern for many cities worldwide. To manage port noise, city governments, port authorities, and even academic researchers have formulated various strategies at different scales. Most of these strategies have been published in a variety of formats (e.g., academic articles, acoustic reports, and governance guidelines), and to our knowledge, have never been compiled and reviewed. The present paper is a review of available documentation and literature to identify trends and practices in port noise management. A Web of Science search and additional reference list checks yielded a total of 69 documents, out of which we identified 41 documents for full review. We found that three main types of strategies are suggested or implemented, namely (1) technical solutions (e.g., noise barriers), (2) design and planning considerations (e.g., traffic calming, buffer zones), and (3) processes and operations (e.g., communication with residents, hours of operation). Few of the suggested or implemented strategies to mitigate noise port are evaluated after implementation. Nearly all of the evaluations rely on acoustic measurements and overlook resident experiences. We discuss ways to better involve residents to improve sonic cohabitation.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".