Bibliographic record
Abstract
Sriharsha Gummadi, MD, has joined the Division of Acute Care Surgery. Dr. Gummadi is a graduate of the Sidney Kimmel Medical College at Thomas Jefferson University. He then went on to complete residency at Lakenau Hospital and fellowship at the University of Pennsylvania. He is double board certified in Surgery and Surgical Critical Care and cares for patients at the Thomas Jefferson University Hospital and Jefferson Torresdale Hospital. David Kim, MD, has joined the Division of Plastic Surgery. Dr. Kim is a graduate of the University of Maryland School of Medicine. He then went on to complete general surgery residency at Brown University, Rhode Island Hospital, followed by plastic surgery residency at Temple University Hospital, and fellowship in craniomaxillofacial surgery at University of Toronto, Hospital for Sick Children. He cares for patients at Thomas Jefferson University Hospital and is a Pride Care Affirming Clinician. Megan E. Lundy, MD, has joined the Division of Acute Care Surgery. Dr. Lundy is a graduate of the Sidney Kimmel Medical College at Thomas Jefferson University. She then went on to complete residency at Wake Forest University School of Medicine and fellowship at the University of Arizona College of Medicine. She is double board certified in Surgery and Surgical Critical Care and cares for patients at the Thomas Jefferson University Hospital and Jefferson Torresdale Hospital. Richard Zheng, MD, MHS, MS, has joined the Division of Surgical Oncology. Dr. Zheng is a graduate of the Johns Hopkins Bloomberg School of Public Health (MHS in Biochemistry and Molecular Biology) and SUNY at Stony Brook School of Medicine. He completed residency and a Master of Science degree in clinical pharmacology concurrently at Thomas Jefferson University followed by a fellowship in Surgical Oncology and Hepatobiliary Surgery at Johns Hopkins Hospital. He cares for patients at the Thomas Jefferson University Hospital and
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.786 | 0.652 |
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".