Use your best endeavours to discover a sheltered and safe harbour
Bibliographic record
Abstract
Abstract On 24 May 1847, Sir John Franklin’s third expedition reported “All well”, but less than a year later, on 22 April 1848, the 129 sailors who had set out from Britain on Erebus and Terror had been reduced to 105 survivors departing their frozen ships in a desperate attempt to escape the Arctic. At least 24 were so unhealthy that they would perish after having travelled little more than 100 km from the ships. By contrast, the small mortality rates on other contemporary Arctic expeditions, some of which stayed in the Arctic considerably longer, were consistent with the mortality rates in the Royal Navy worldwide. This paper explores the question of what difference caused so many of Franklin’s crew to die during their final months on-board the ships and in the initial stages of the escape attempt. From the perspective of cultural ecology, the most significant difference, and the ultimate cause of the catastrophe as it unfolded, was wintering in the ice pack. This distinguished the Franklin expedition from all of the other comparable overwintering expeditions, and precluded the Erebus and Terror crews from hunting or fishing. That in turn led to nutritional deficiencies due to much greater reliance on stored provisions than other expeditions.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".