Bibliographic record
Abstract
It is both true and efficient that sensorineural systems respond to change and little else. Perceptual systems do not record absolute level be it loudness, pitch, brightness, or color. This fact has been demonstrated in every sensory domain. For example, the visual system is remarkable at maintaining color constancy over widely varying illumination such as sunlight and varieties of artificial light (incandescent, fluorescent, etc.) for which spectra reflected from objects differ dramatically. Results will be reported for a series of experiments demonstrating how auditory systems similarly compensate for reliable characteristics of spectral shape in acoustic signals. Specifically, listeners’ perception of vowel sounds, characterized by both local (e.g., formants) and broad (e.g., tilt) spectral composition, changes radically depending upon reliable spectral composition of precursor signals. These experiments have been conducted using a variety of precursor signals consisting of meaningful and time-reversed vocoded sentences, as well as novel nonspeech precursors consisting of multiple filter poles modulating sinusoidally across a source spectrum with specific local and broad spectral characteristics. Constancy across widely varying spectral compositions shares much in common with visual color constancy. However, auditory spectral constancy appears to be more effective than visual constancy in compensating for local spectral fluctuations. [Work supported by NIDCD DC-04072.]
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".