I'm All Ears: Common Allergens in Wireless in-Ear Headphones
Bibliographic record
Abstract
Dermatitis®Ahead of Print Pearls & ZebrasI'm All Ears: Common Allergens in Wireless in-Ear HeadphonesCamila N. Fontane Hoyos and Ari M. GoldminzCamila N. Fontane Hoyoshttps://orcid.org/0009-0007-5102-9910From the Contact Dermatitis and Occupational Dermatology Program, Brigham and Women's Hospital, Boston, Massachusetts, USA.Search for more papers by this author and Ari M. GoldminzAddress reprint requests to Ari M. Goldminz, MD, Department of Dermatology, Harvard Medical School, 850 Boylston Street, Suite 130, Chestnut Hill, MA 02467, USA. E-mail Address: [email protected]https://orcid.org/0000-0001-7910-1901From the Contact Dermatitis and Occupational Dermatology Program, Brigham and Women's Hospital, Boston, Massachusetts, USA.Department of Dermatology, Harvard Medical School, Chestnut Hill, Massachusetts, USA.Search for more papers by this authorPublished Online:16 Feb 2024https://doi.org/10.1089/derm.2023.0251AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 0Issue 0 Information© 2024 American Contact Dermatitis Society. All Rights Reserved.To cite this article:Camila N. Fontane Hoyos and Ari M. Goldminz.I'm All Ears: Common Allergens in Wireless in-Ear Headphones.Dermatitis®.ahead of printhttp://doi.org/10.1089/derm.2023.0251Online Ahead of Print:February 16, 2024 PDF download
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.015 |
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".