Bibliographic record
Abstract
Abstract It was a strange accident that gave an alert amateur physiologist access for the first time to the digestive processes in the human stomach. William Beaumont was an American military surgeon in the first half of the nineteenth century. He was the product of the prevailing system of training, when aspiring doctors qualified not by going to medical school but by serving an apprenticeship in the private practice of a physician or surgeon. While in post as surgeon at a fort in a remote fastness in Michigan, Beaumont was summoned one evening to tend the victim of an accident in a nearby fur trading post. Alexis St Martin, a young Canadian from the far north, had been hit by a shot-gun blast at close range and was lying unconscious in a pool of blood when Beaumont arrived. Wadding, shot, and fragments of clothing had penetrated his ribcage and stomach, creating a hole through which a man’s fist could pass. To general astonishment the victim did not die, but because he was not strong enough to work, the authorities at the trading post, unwilling to support an invalid, resolved to send him home to Canada. Beaumont doubted whether St Martin would survive the journey of 2,000 miles, and so took him in, ‘nursed him, fed him, clothed him, lodged him and furnished him with every comfort, and dressed his wounds daily and for the most part twice a day’.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".