Waiting to Exhale: Chaos, Toxicity and the Origins of the U.S. Chemical Warfare Service
Bibliographic record
Abstract
In 2008, Susan L. Smith published “Mustard Gas and American Race-Based Human Experimentation in World War II.” Research, undertaken by the US Army, attempted to quantify the effect of mustard gas (actually a volitile liquid) and othe chemical agents on people from different racial groups. This was based on the idea that different races would respond differently to the toxins, and in particular that this would be evident through dermal reaction. In other words, different skin color might mean different skin constitution. Some of the testing seemed reasonable, since new chemicals and equipment had been developed since 1919, and the racial issue added another dimension to the research. On closer examination, the testing was primarily based on old chemical agents such as mustard gas, Lewisite and phosgene, and thus the extent of the testing seemed scientifically and medically unnecessary. The chemical agents had been developed, tested, used in battle, the wounded treated and the dead subjected to detailed pathological study. The major combatants in World War I had all committed extensive scientific resources to the study of these agents looking at both offensive and defensive aspects of their use, including toxicity testing. The U.S. Chemical Warfare Service (CWS) had been formed in 1918 to specifically deal with issues such as toxicity tests, so why was the U.S. Army revisiting the subject of chemical weapons testing during World War II?
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".