Bibliographic record
Abstract
Historians of the First World War have often dismissed the important role of poison gas in the battles of the Western Front. In No Place to Run, however, Tim Cook shows that the serious threat of gas did not disappear with the introduction of gas masks. By 1918, gas shells were used by all armies to deluge the battlefield, and those not instructed with a sound anti-gas doctrine left themselves exposed to this new chemical plague. Cook uses fascinating primary sources -- diaries, letters, reminiscences, published memoirs, and the official archival record -- to illustrate the horror of gas warfare for the average trench soldier. As the first chlorine clouds rolled across the fields during the 2nd Battle of Ypres, soldiers were forced to stuff urine-soaked handkerchiefs in their mouths in order to survive. As the gas war evolved, mustard gas plagued the soldiers at the front as it lay active in mud and snow for weeks on end. There was no escape from the pervasive nature of poison gas. Entering the dug-outs, it attacked men when they were least ready. In response, the Canadian Corps had to develop an anti-gas doctrine, a process that Cook describes in full. No Place to Run provides a challenging re-examination of the function of gas warfare in the First World War, including its important role in delivering victory in the campaign of 1918 and its curious postwar legacy. It will be of interest both to historians and military buffs.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.207 | 0.097 |
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".