Bibliographic record
Abstract
The chapter begins with epidemics of influenza in Maine from 1758 and is introduced by a reference to Noah Webster’s A Brief History of Epidemic and Pestilential Diseases (1799). Barker discusses his and other Maine physicians’ experience from 1780–1795 with cancer, predominantly of skin, node, and breast, seldom treated with success. He details a Portland newspaper article of 1818 graphically describing the last days of a woman’s seventeen-year battle with a lip cancer and refers to a book by a Dr. William Steward of Canaan, Maine reporting forty-seven cancers extracted “without a knife.” Finally, Barker relates his 1795 experience of obtaining a tainted quarter of veal that became palatable after soaking overnight in lye (alkali). With his reading of Lavoisier’s chemistry, Barker began to treat his patients successfully with alkalis to rid them of the “septic poisons.” He recorded his experience and sent a letter to New York City, where it was read by Professor Samuel Mitchill at Columbia University. Mitchill, an editor of the first US medical journal, the Medical Repository, published Barker’s letter, the first of many to be published in that journal over the next twenty years.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".