Science and the Pacific War : science and survival in the Pacific, 1939-1945
Bibliographic record
Abstract
Preface. Introduction: Science, Technology and the War in the Pacific R. MacLeod. Part I: The Scientists go to War. 1. Combat Science: OSRD's Postscript in the Pacific R. MacLeod. 2. The Smithsonian Goes to War: The Increase and Diffusion of Scientific Knowledge in the Pacific P.M. Henson. 3. Malaria in the Southwest Pacific in World War II M.E. Condon-Rall. 4. The Machine in the Pacific: The Diverse Legacy of Technology D.T. Fitzgerald. 5. The Role of Botanists During World War II in the Pacific Theatre R.A. Howard. Part II: The War Down Under. 6. Australian Universities at War: The Mobilisation of Universities in the Battle for the Pacific M. Freeman. 7. Australia's Mustard Gas Guinea Pigs B. Goodwin. 8. Technological Transfer and the War in the Pacific I.D. Rae. 9. Managing the Impact of War: Australian Anthropology and the South West Pacific G.G. Gray. 10. New Zealand Scientists in Action: The Radio Development Laboratory and the Pacific War R. Galbreath. Part III: The Unseen War. 11. Canadian Scientists, CBW Weapons and Japan, 1939-1945 D. Avery. 12. The American Cover-up of Japanese Human Biological Warfare Experiments, 1945-1948 S.H. Harris. 13. The Role of Scientific Intelligence in the Pacific War F. Cain. 14. The Useful War: Radar and the Mobilization of Science and Industry in Japan M.F. Low. Bibliography.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".