Bibliographic record
Abstract
After he retired from his career as an ophthalmologist in 1920, Dr Casey Wood devoted himself to his two private passions – ornithology and book collecting. He attended auctions, badgered book dealers, and haunted bookshops wherever he travelled with his wife Emma Shearer Wood. In 1926 Wood turned to Wheldon and Wesley and their agent, William John Henry Craddock (1870-1941), to help him acquire a magnificent collection held by the bookseller Quaritch of over 900 large, colourful and often life-size drawings of exotic birds, animals and fish, and equally imposing images of flowers, insects, and fungi, loosely inserted in twenty-nine portfolios, and painted by arguably the finest animal and botanical painters of eighteenth-century Britain. The collection had been created by the British jurist and collector Taylor White FRS (1701-72). This article recounts how White accumulated his collection, and how Wood acquired it for the Blacker Wood Natural History Collection at McGill University Library in Montreal. It also describes how White catalogued his collection, and how subsequent dealers and librarians added their own layers of documentation, up to the digitization of the paintings and manuscript notes by the McGill Library, which has made them available to contemporary researchers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.187 | 0.030 |
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".