Bibliographic record
Abstract
Ludmila Nahar, MD, Department of Dermatology, School of Medicine, University of Mississippi Medical Center, Jackson, MS. Robert T. Brodell, MD, FAAD, Department of Dermatology, School of Medicine, University of Mississippi Medical Center, Jackson, MS Robert Brodell, MD, discloses the following potential conflicts of interest: (a) multicenter clinical trials: Galderma Laboratories, LP, principal investigator; Novartis, principal investigator; and Glaxo Smith Kline, principal investigator; (b) editorial boards: American Medical Student Research Journal, Practice Update Dermatology, Practical Dermatology, Journal of the Mississippi State Medical Society, SKIN: The Journal of Cutaneous Medicine, and Journal of the American Academy of Dermatology. There are no conflicts of interest related to employment; stock ownership; expert testimony; grants; patents filed, received, pending, or in preparation; or royalties. Ludmila Nahar, MD, declares no conflict of interest. Correspondence concerning this article should be addressed to Robert T. Brodell, MD, FAAD, Department of Dermatology, School of Medicine, University of Mississippi Medical Center, 2500 North State Street – L216, Jackson, MS 39216 USA. E-mail: [email protected]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".