Bibliographic record
Abstract
Currently, there are very few guidelines on parameters needed to create an effective auditory display. Auditory displays can be intrusive and may not be used effectively if they are poorly designed. However, music is often in our environments as ambient noise and, instead of being intrusive, can be perceived as making the environment calmer and more productive. We present the initial steps of exploring the option of using music as a medium to develop an auditory display capable of conveying normal state information and warning information. An important feature that may impact the effectiveness of auditory warnings is perceived urgency: the impression of urgency that a sound evokes on a listener. To explore whether music could convey urgency as needed for auditory warnings, we evaluated four different musical phrases that varied in time and key signature as a method of measuring the effects of mode and tempo on perceived urgency. The effectiveness of the study was tested with twenty subjects split into a two by two factorial design: gender (male vs. female) and musical experience (experienced vs. non-experienced). The applications of this research can help develop concrete guidelines when designing effective auditory displays in order to improve users’ performance when dealing with complex interfaces.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".