The Voice of Skogula in ‘Beasts Royal’ and a Story of the Tagus Estuary (Lisbon, Portugal) as Seen through a Whale’s-Eye View
Bibliographic record
Abstract
Patrick O’Brian inspired this work, with his 1934 book of chronicles “Beasts Royal,” where he gives a voice to animals. Therein, among other animals, we find Skogula, a young sperm whale journeying with his family group across the South Seas and his views on the surrounding world, both underwater and on land. This paper tells a story of historical natural events, from the viewpoint of a fin whale that travelled, rested and stranded in the Tagus estuary mouth (Lisbon, Portugal) during the early 16th century. It allows us to move across time and explore the past of this estuarine ecosystem. What kind of changes took place and how can literature and heritage contribute to understand peoples’ constructions of past environments, local maritime histories and memories? In the second part of this essay we present a fictional short story, supported on historical documental sources and imagery research where Lily, the whale, is the main character. Thus, we see the Tagus estuary as perceived through this whale’s-eye view. Finally, we discuss past earthquakes, whale strandings, the occurrence of seals and dolphins and peoples’ perceptions of the Tagus coastal environment across time. We expect to make a contribution to the field of the marine environmental humanities. We will do so both by addressing, by means of this literary approach, the writing of “new thalassographies,” oceanic historiographies and “historicities” and by including all intervening actors—people, animals and the physical space—in the understanding of the past of more-than-human aquatic worlds.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".