Bibliographic record
Abstract
Natural sounds have substantial acoustic structure (predictability, nonrandomness) in their spectral and temporal compositions. Listeners are expected to exploit this structure to distinguish simultaneous sound sources; however, previous studies confounded acoustic structure and listening experience. Here, sensitivity to acoustic structure in novel sounds was measured in discrimination and identification tasks. Complementary signal-processing strategies independently varied relative acoustic entropy (the inverse of acoustic structure) across frequency or time. In one condition, instantaneous frequency of low-pass-filtered 300-ms random noise was rescaled to 5 kHz bandwidth and resynthesized. In another condition, the instantaneous frequency of a short gated 5-kHz noise was resampled up to 300 ms. In both cases, entropy relative to full bandwidth or full duration was a fraction of that in 300-ms noise sampled at 10 kHz. Discrimination of sounds improved with less relative entropy. Listeners identified a probe sound as a target sound (1%, 3.2%, or 10% relative entropy) that repeated amidst distractor sounds (1%, 10%, or 100% relative entropy) at 0 dB SNR. Performance depended on differences in relative entropy between targets and background. Lower-relative-entropy targets were better identified against higher-relative-entropy distractors than lower-relative-entropy distractors; higher-relative-entropy targets were better identified amidst lower-relative-entropy distractors. Results were consistent across signal-processing strategies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.014 | 0.009 |
| Insufficient payload (model declined to judge) | 0.839 | 0.867 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".