The development of noise-induced hearing loss in military trades
Bibliographic record
Abstract
An investigation is in progress to determine risk factors for the development of noise-induced hearing loss in Canadian Forces personnel. A total of 1057 individuals representing a wide range of military trades have contributed their current audiogram, first audiogram on record, and responses to a 56-item questionnaire. The protocol for the hearing test was standardized and conformed to current audiological practice. The items included in the questionnaire related to demographics, occupational and nonoccupational noise exposure history, training in and utilization of personal hearing protectors, and factors other than noise which might affect hearing (e.g., head injury, ear disease, exposure to solvents, and the use of medications). Analyses are underway to determine the average current hearing thresholds as a function of frequency and change relative to baseline values at recruitment for groups defined by trade, rated noise hazard, and years of service. Preliminary results suggest ways to improve the training of personnel with respect to the effects of both occupational and nonoccupational noise exposure and methods of implementing hearing conservation strategies. The role of head injury, history of ear disease, and the use of medications appear to be small. [Work supported by Veterans Affairs Canada.]
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".