Bibliographic record
Abstract
In 1946, Tom Brock spent part of his summer dumping mustard gas bombs off a barge into the Atlantic Ocean. Brock was a civilian employed by the United States Army Transport Service in Charleston, South Carolina. His job was to dispose of surplus bombs and drums filled with mustard gas. Sulphur mustard, commonly called “mustard gas,” can take several forms: a liquid, a solid, or a vapour. Mustard gas, named for its mustard-like color and smell, is a vesicant that is toxic to humans and causes blistering and burns, affecting the lungs, eyes, and skin. Brock recalled that he and the soldiers enjoyed watching the occasional bomb explode as it sunk into the water. “We thought it was fun,” explained Brock. “I was 18 or 19 years old. We weren’t scared. We didn’t fear any explosive. We thought we were immortal.” Later that summer he was required to guard a barge of bombs that were leaking mustard gas, which looked to him like hot molasses. Due to the known health risks, Brock was told to wear a protective suit and gas mask. However, it was a hot day so he loosened the straps around his legs. As a result, enormous blisters developed, swelling out like a balloon from his toes to his knees. His summer job was no longer fun as he experienced firsthand the health hazards of exposure to mustard gas.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".