Bibliographic record
Abstract
Some years ago I wrote about the medical material in Alexandre Dumas’s The Count of Monte Cristo and indicated that it was not only rich and varied but remarkably accurate for a non-physician writer.1, 2 For example, he described the locked-in syndrome in the character Monsieur Noirtier de Villefort 120 years before the seminal article by Plum and Posner in 1966. A list of some of the conditions, medications and procedures in the The Count of Monte Cristo is shown in the table. View this table: Table Medical matters in The Count of Monte Cristo As a young aspiring writer, Dumas learned about medicine from a young medical graduate of the University of Paris, Dr A Thibauld, who taught him anatomy, physiology, toxicology and other medical facts in his rooms each evening. Dr Thibauld also took Dumas on hospital medical rounds to see patients suffering from various ailments. Dumas had a writer’s interest in observing cases as potential material, learning the details of conditions he could later use in his novels. As he said in his autobiography, he used the lessons from Dr Thibauld in his writings for the next 30 years.3 Alexandre Dumas was a remarkable man who led a remarkable life and left a body of lasting literature that is probably greater than any other writer. He wrote over 600 books (no one is sure how many), and no one has read all of Dumas. He wrote constantly and often four or five books at a time, publishing a shelf of books …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".