Bibliographic record
Abstract
Breeding Guinea Pigs for Food, Fur and FancyIn 1897 Lloyd's Weekly Newspaper published an article on 'Preparing Guinea Pigs for Exhibition'.Until recently, the writer explained, 'The Guinea Pig was thought so little of that it only vied with white mice in the affection of animal-loving schoolboys' (Figure 1).Beginning in the 1880s, however, the species had 'been taken in hand by the fancier' and it was now 'bred to such perfection in colour, shape and other points that none but very good specimens in the best of condition st[ood] a chance of winning a prize at exhibitions'.To improve readers' chances of outclassing the opposition, the article furnished advice on how to spruce up guinea pigs for competition, accentuating the defining features of the three principal breeds.So-called English guinea pigs, 'the coat of which must be short, silky and glossy, must be well brushed daily from head to foot with a soft brush'.'Peruvian' guinea pigs, whose coats were 'long, straight and silky' should be 'bathed with warm water', any knots in their fur 'picked out with a darning needle'.Rough-coated 'Abyssinians' should be backcombed to emphasise the 'harshness' of their 'wiry' pelage, and their 'rosettes…gently brushed out with a damp toothbrush'.('Preparing Guinea Pigs for Exhibition' 8).
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".