Supplemental text messaging for the resolution of auditory overload
Bibliographic record
Abstract
Military signal operators listen, transcribe, and respond to audio traffic over multiple audio channels, in high-level noise from vehicles and weapons. The messages typically overlap in time and may be difficult to disentangle. Two studies were carried out to determine the benefit of supplemental texting. Normal-hearing participants were tested in a mock up of a military command post. Brief messages were played simultaneously over a communications headset (dichotic) and a loudspeaker array, either in quiet or in a background of vehicle noise. The at-ear speech-to-noise ratio was 5 dB. Only those messages beginning with a pre-assigned call sign were encoded. Mean scores of 84% or better were observed for messages presented over the headset, although there was a clear right ear advantage in noise. Messages coming over the loudspeakers were more difficult to understand but a visual cue directing attention to the source of an incoming targeted message resulted in a significant improvement of 7%. Replacing audio messages over the loudspeakers in noise with visual or audiovisual presentations resulted in an improvement from 71% to 96% that did not negatively affect headset performance. The data suggest that texting is a viable option for communication in cases of degraded audio.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".