Bibliographic record
Abstract
Noise is omnipresent and impacts us all in many aspects of daily living. Noise can interfere with communication not only in industrial workplaces, but also in other work settings (e.g. open-plan offices, construction, and mining) and within buildings (e.g. residences, arenas, and schools). The interference of noise with communication can have significant social consequences, especially for persons with hearing loss, and may compromise safety (e.g. failure to perceive auditory warning signals), influence worker productivity and learning in children, affect health (e.g. vocal pathology, noise-induced hearing loss), compromise speech privacy, and impact social participation by the elderly. For workers, attempts have been made to: 1) Better define the auditory performance needed to function effectively and to directly measure these abilities when assessing Auditory Fitness for Duty, 2) design hearing protection devices that can improve speech understanding while offering adequate protection against loud noises, and 3) improve speech privacy in open-plan offices. As the elderly are particularly vulnerable to the effects of noise, an understanding of the interplay between auditory, cognitive, and social factors and its effect on speech communication and social participation is also critical. Classroom acoustics and speech intelligibility in children have also gained renewed interest because of the importance of effective speech comprehension in noise on learning. Finally, substantial work has been made in developing models aimed at better predicting speech intelligibility. Despite progress in various fields, the design of alarm signals continues to lag behind advancements in knowledge. This summary of the last three years' research highlights some of the most recent issues for the workplace, for older adults, and for children, as well as the effectiveness of warning sounds and models for predicting speech intelligibility. Suggestions for future work are also discussed.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".