The health of nine Royal Naval Arctic crews, 1848 to 1854: implications for the lost Franklin Expedition
Bibliographic record
Abstract
ABSTRACT Medical factors including tuberculosis, scurvy, lead poisoning and botulism have been proposed to explain the high death rate prior to desertion of the ships on Sir John Franklin's expedition of 1845–1848 but their role remains unclear because the surgeons’ Sick books which recorded illness on board have eluded discovery. In their absence, this study examines the Sick books of Royal Naval search squadrons sent in search of Franklin, and which encountered similar conditions to his ships, to consider whether their morbidity and mortality might reflect that of the missing expedition. The Sick books of HMS Assistance, Enterprise, Intrepid, Investigator, Pioneer and Resolute yielded 1,480 cases that were coded for statistical analysis. On the basis of the squadrons’ patterns of illness it was concluded that Franklin's crews would have suffered common respiratory and gastro-intestinal disorders, injuries and exposure and that deaths might have occurred from respiratory, cardiovascular and tubercular conditions. Scurvy occurred commonly and it was shown that the method of preparing ‘antiscorbutic’ lemon juice for the search squadrons and Franklin's ships would have reduced its capacity to prevent the disease but there were no grounds to conclude that scurvy was significant at the time of deserting the ships. There was no clear evidence of lead poisoning despite the relatively high level of lead exposure that was inevitable on ships at that time. There was no significant difference between the deaths of non-officer ranks on Franklin's ships and several of the search ships. The greater number of deaths of Franklin's officers was proposed to be more probably a result of non-medical factors such as accidents and injuries sustained while hunting and during exploration.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".