Daniel Smith Lamb (1843–1929): A window into the early histories of the Army Medical Museum and Howard University Medical School
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
U.S. Army doctor Daniel Smith Lamb was a significant figure in the history of American pathology during its formative years. For 55 years (1865-1920), Lamb performed hundreds of autopsies in and around Washington, D.C. and personally collected over 1,500 gross pathology specimens for the Army Medical Museum. His work began at the close of the Civil War and continued on through World War I, contributing substantially to gross pathological and histological studies that documented wartime pathology, thus further contributing to the training of Army doctors. Specimens he collected also include material from autopsies he conducted on President James Garfield, his assassin Charles Guiteau, and other historical figures. Under the auspices of the Army Medical Museum, he conducted autopsies across the city of Washington for the museum's collection, many of which survive to this day at the National Museum of Health and Medicine. He served under 12 U.S. Army Surgeons General and 11 Museum Curators and was noted to be a steadying influence during a time of constant leadership changes at that institution. Lamb was known throughout Washington, D.C. as an advocate of medical education for African-Americans and women. While working at the Museum, he simultaneously served for 46 years as professor of anatomy at Howard University (1877-1923). He wrote seminal histories of the institutions with which he was associated and in so doing also contributed significantly to the study of the history of medicine.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 it