Bibliographic record
Abstract
In 1609, while exploring the lake that now bears his name, Samuel de Champlain looked to the east and exclaimed, “Voila les vert monts.” The Green Mountain State was an inhospitable place at that time; few dared to settle there until after the end of the French and Indian War, in 1763. By 1771, the population of Vermont was 4667 people. They led a difficult existence, and the earliest surgeons were true pioneers. Most doctors of the time practiced in widespread areas and had to endure difficult conditions to reach their patients. Many anecdotes about these individuals have survived. 1-3 Dr Adam Johnson thwarted an attack by wolves by throwing his saddlebags at them. Dr Steven Powers of Woodstock was physically adapted to the challenges of the countryside. He wore buckskin trousers for their durability and as a convenient way to sharpen his surgical instruments. Vermont’s surgical history is inextricably linked to the political history of New England, New York, and southern Canada. Like many Vermont surgeons, Dr Powers participated in the American Revolution and was a surgeon during the Battle of Bunker Hill. 1 Dr Silas Hodges of Clarendon served with George Washington’s Continental Army. However, not all Vermont surgeons were loyal to the new republic. Dr Frederick Aubrey of Bradford served as a surgeon in the British Army and was credited with caring for General Wolfe’s wounds at Quebec. Dr Jacob Roebuck of Grand Isle was a surgeon for the German contingent of King George III’s army. In 1741, Vermont’s land area was claimed by New Hampshire. 4 New York disputed New Hampshire’s claim, and in 1764, the English Crown decided that the land west of the Connecticut River belonged to New York. The citizens of Vermont were upset over the rules imposed by the New York County courts against local government. Ethan Allen, leader of the New Hampshire settlers in Vermont, organized the Green Mountain Boys to protect their interests, the same group who fought so ardently at the battles of Bennington, Ticonderoga, and Crown Point.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 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".