Inhalation toxicity in mice exposed to sarin (GB) for 20–720 min
Bibliographic record
Abstract
Most of the historical data for the toxicity of sarin (GB) was collected for exposure times of <10 min in attempts to establish the utility of and defence against this agent in offensive military use. However, information concerning the toxicity of GB (and other nerve agents) from longer exposures of 1-12 h is critical for all personnel who must work in or close to low-level concentrations of chemical for extended periods and for all personnel, dressed in Individual Protective Equipment, who need to know when, and if, it is safe to take off these cumbersome garments.The data presented for the toxicity of GB to mice for whole-body exposures of 20 min to 12 h are intended to form part of an ongoing, multi-species effort aimed at establishing toxicity estimates for humans for these longer exposure times: LCT50 values of 430, 540, 900, 1210 and 2210 mg.min m(-3) or LC50 values of 21.5, 9.0, 5.0, 3.4 and 3.1 mg m(-3) were obtained for mice for 20-, 60-, 180-, 360- and 720-min exposures to GB, respectively. The data for longer exposures do not follow Haber's rule (LCT50=CT). The 20- and 60-min data fit the 'toxic load model' involving CnT that was established previously from historical data for 0.17-30 min GB exposures to mice. The LCT(50) and LC50 values for 3, 6 and 12 h are progressively higher (toxicity lower) than predicted by either Haber's rule or the toxic load model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".