Advantages and Caveats When Recording Steady-State Responses to Multiple Simultaneous Stimuli
Bibliographic record
Abstract
This article considers the efficiency of evoked potential audiometry using steady-state responses evoked by multiple simultaneous stimuli with carrier frequencies at 500, 1000, 2000, and 4000 Hz. The general principles of signal-to-noise enhancement through averaging provide a basis for determining the time required to estimate thresholds. The advantage of the multiple-stimulus technique over a single-stimulus approach is less than the ratio of the number of stimuli presented. When testing two ears simultaneously, the advantage is typically that the multiple-stimulus technique is two to three times faster. One factor that increases the time of the multiple-response recording is the relatively small size of responses at 500 and 4000 Hz. Increasing the intensities of the 500- and 4000-Hz stimuli by 10 or 20 dB can enhance their responses without significantly changing the other responses. Using multiple simultaneous stimuli causes small changes in the responses compared with when the responses are evoked by single stimuli. The clearest of these interactions is the attenuation of the responses to low-frequency stimuli in the presence of higher-frequency stimuli. Although these interactions are interesting physiologically, their small size means that they do not lessen the advantages of the multiple-stimulus approach.
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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 | 0.079 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".