Large mortality differences between Australian and New Zealand soldiers during 1918-19 influenza pandemic
Bibliographic record
Abstract
The large but variable mortality experienced during the 1918-19 influenza pandemic has not been adequately explained. Military records provide some of the few prospective sources of both morbidity and mortality data from 1918 at the end of the First World War. With a few exceptions, the Australian (Australian Imperial Force, AIF) and New Zealand (New Zealand Expeditionary Force, NZEF) Armies were very similar in training and organization. One of the exceptions was that volunteer Australian recruits were largely trained in England whereas New Zealand had large recruit training camps in New Zealand for conscripts prior to embarkation. The Australian and New Zealand Armies had nearly equal influenza mortality in Europe (6.6 vs. 6.4 deaths / 1000 men) during the 1918-19 influenza pandemic but experienced a nine-fold mortality (1.9 vs. 17.2 deaths / 1000 men) difference in the Southern Hemisphere. Some of the mortality difference can be explained by the earlier arrival of influenza in New Zealand. This striking mortality difference in otherwise very similar military groups is likely to have arisen from their differing training circumstances whereby New Zealand soldiers were newer to the military and thus more immunologically naïve to bacterial respiratory pathogens. These mortality differences in otherwise highly comparable military units highlight the importance of secondary bacterial pneumonia to mortality during influenza pandemics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".