Bibliographic record
Abstract
No AccessPerspectives on Hearing and Hearing Disorders in ChildhoodArticle1 Apr 2004Biological Basis of Noise-Induced Hearing Loss Brenda L Lonsbury-Martin Brenda L Lonsbury-Martin ASHA, Speech-Language-Hearing and ResearchRockville, MD Google Scholar More articles by this author https://doi.org/10.1044/hhdc14.1.4 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationTrack Citations ShareFacebookTwitterLinked In References Ahmad, M., Bohne, B. A., & Harding, G. W. (2003). An in vivo tracer study of noise-induced damage to the reticular lamina.Hearing Research, 175, 82–100. Google Scholar ASHA. Retrieved August 11, 2003, from http://www.asha.org/public/ hearing/disorders/noise.htm Google Scholar Brownell, W. E. (1990). Outer hair cell electromotility and otoacoustic emissions.Ear and Hearing, 11, 82–92. CrossrefGoogle Scholar Caiazzo, A. J., & Tonndorf, J. (1978). Ear canal resonance and temporary threshold shift.Otolaryngology, 86, ORL-820. 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Google Scholar Zheng, J. L., & Gao, W. Q. (2000). Overexpression of Math1 induces robust production of extra hair cells in postnatal rat inner ears.Nature Neuroscience, 3, 580–586. CrossrefGoogle Scholar Additional Resources FiguresReferencesRelatedDetails Volume 14Issue 1April 2004Pages: 4-9 Get Permissions Add to your Mendeley library History Published in issue: Apr 1, 2004 Metrics Topicsasha-topicsasha-sigsasha-article-typesleader-topicsCopyright & Permissions© 2004 American Speech-Language-Hearing AssociationPDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".