Bibliographic record
Abstract
That occupational noise exposure causes hearing loss has long been known, but non-occupational noise exposure was not recognized as a problem until the 1960s. Today, most Americans are regularly exposed to non-occupational noise sufficient to cause hearing loss, perhaps because of an erroneous belief that 85-decibel noise exposure, based on occupational standards, is safe for the public without time limit. Common noise sources include personal audio systems, especially among young people; public transit; social, sports, and entertainment venues; household appliances; and power tools and landscape maintenance equipment. As a result, approximately 25% of American adults age 20-69 have noise-induced hearing loss, 53% without significant occupational exposure. Why? The Equal Energy Hypothesis states that equal amounts of sound energy produce equal amounts of hearing loss, regardless of how that sound is distributed over time. The response to sound is non-linear, though, and brief high-level exposures may have disproportionate impacts on hearing. How loud is too loud? The Auditory Injury Threshold is only 75-78 A-weighted decibels. The Environmental Protection Agency calculated a time-weighted daily average of 70 decibels to prevent hearing loss, but the true safe noise level may be 60 decibels or lower. Recommendations are made to reduce public noise exposure.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".