A statistical model for prediction of functional hearing abilities in real-world noise environments
Bibliographic record
Abstract
Many tasks require functional hearing abilities such as speech communication, sound localization, and sound detection, and are performed in challenging noisy environments. Individuals who must perform these tasks and whose functional hearing abilities are impaired by hearing loss may constitute safety risks to themselves and others. We have developed and validated in two languages (American English and Canadian French) statistical techniques based on Plomps (1986) speech reception threshold model of speech communication handicap. These techniques predict functional hearing ability using the statistical characteristics of the real-world noise environments where the tasks are performed together with the communication task parameters. The techniques will be used by the Department of Fisheries and Oceans Canada to screen individuals who are required to perform hearing-critical public safety tasks. This presentation will summarize the three years of field and laboratory work culminating in the implementation of the model. Emphases will be placed on the methods for statistical characterization of noise environments, since these methods may allow generalization of the model to a wider range of real-world noise environments. [Work sponsored by Department of Fisheries and Oceans 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".