‘Um Grande Peixe, Dona Baleia da Costa’: The Whale in Portuguese Early Modern Natural History
Bibliographic record
Abstract
Several early modern sources for a natural history of whales in Portugal are now emerging in a variety of forms – news, memoirs, poems, and studies – with clear, new, and rich references to ichthyology. The large cetaceans, when they beached on the Portuguese coast or accidentally entered the Tagus River (Lisbon), aroused the curiosity of the common people, of nobles and scholars. Whales and the resources that were extracted from them were important for food, lighting, and apothecaries, and the accumulation of knowledge about them and technology to hunt and process them was valuable. Some written sources, mostly the ones that aimed at an erudite audience and at describing the biology and behaviour of marine animals, were likewise accompanied by illustrations. Many of these publications were adapted to local contexts and translated into other vernacular languages from Portuguese, while others were lost in time almost to the present day (such as the 18th-century manuscript entitled Piscilegio Lusitano). Nevertheless, in Lisbon and Portugal, whales gained a cultural existence beyond their biological life. These large and fascinating animals enriched the local cultural life, being a metaphor to address moral issues, the motto of poems (such as the one called Dona Baleia da Costa) and became main characters of the real processions of people who went to watch the beached whales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".