Bibliographic record
Abstract
George Stubbs's painting The Duke of Richmond's First Bull Moose (1770) offers a visual counterpart to an arresting account addressed to the naturalist Thomas Pennant in Gilbert White's Natural History of Selborne of his 1768 visit to inspect the Duke of Richmond's recently deceased female “moose-deer.” The Duke was one of several aristocrats who acquired moose from Canada; his animals were studied by White, by Pennant, and by the physician and anatomist William Hunter who commissioned Stubbs's work. While Stubbs depicted the animal accurately, he had no knowledge of its natural habitat; the moose is incongruously placed in a mountainous lake landscape during an approaching storm. This romanticized landscape prospect offers a metaphor for the story of the moose in late eighteenth-century Britain. While looking forward and outward to understand the natural world, these depictions of the moose are equally subjective and imaginative and contributed to debates on extinction and the idea of species. The unsuccessful introduction of the moose to Britain defeated prospects for their domestication and cross-breeding. Close reading of Stubbs's painting, White's narrative, Pennant's Arctic Zoology, and Hunter's unpublished scientific paper on the moose suggest the kinds of wonder which mark the efforts on the part of science and the arts to understand this puzzling and mysterious animal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".