Enriched Acoustic Environment after Noise Trauma Reduces Hearing Loss and Prevents Cortical Map Reorganization
Bibliographic record
Abstract
Exposure to sound of sufficient duration and level causes permanent damage to the peripheral auditory system, which results in the reorganization of the cortical tonotopic map. The changes are such that neurons with pre-exposure tuning to frequencies in the hearing loss range now become tuned to frequencies near the near-normal lower boundary of the hearing loss range, which thus becomes over represented. However, cats exposed to a traumatizing noise and immediately thereafter placed for a few weeks in an enriched acoustic environment presented a much-restricted hearing loss compared with similarly exposed cats that were placed for the same time in a quiet environment. The enriched environment spectrally matched the expected hearing loss range and was approximately 40 dB above the level of the expected hearing loss. The hearing loss in the quiet environment-reared cats ranged from 6 to 32 kHz with the largest loss (on average, 40 dB) ranging from 24 to 32 kHz. In contrast, the hearing loss in the enriched-environment cats was restricted to 6-8 kHz at a level of, on average, 35 dB and with 16-32 kHz having normal thresholds. Despite the remaining hearing loss for the enriched-environment cats in the 6-8 kHz range, plastic tonotopic map changes in primary auditory cortex could no longer be demonstrated, suggesting that the enriched acoustic environment prevents this reorganization. This finding has implications for the treatment of hearing disorders, such as tinnitus, that have been linked to cortical tonotopic map reorganization.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".