Emergency response plane of toxic gas releases with considering ventilation ratio and meteorological conditions
Bibliographic record
Abstract
A major accident of high toxic gas, leaking of 20 tons of hydrofluoric acid [HF], was happened on 27 September 2012 in Gumi, about 200 km from Seoul, South Korea. The accident killed five workers at the site and severely injured at least 18 others, including workers and emergency personnel. The initial government response to the accident, including possible mistakes by firefighters and a sluggish evacuation of nearby residents, was sharply criticized in the Korean media. The number of industries using toxic gas also has significantly increased most of them located near inhabited areas. These facts are a huge challenge for administrations who must minimize risks around toxic gas facilities where major accidents can occur, and must provide a safe community emergency response. When toxic gas is released during for a certain time, the resident downwind area should be evacuated or stayed in a building with sealing doors and windows, and wait until the toxic gas puff has gone. The criterion for evacuating or not of residence in the building is not setup until now, which might depend on the building and meteorological conditions. In this work, we analysis the toxic gas concentration in the building with toxic gas atmospheric dispersion to help that the local communities are likely to be advised to go indoors and to close windows and doors until given further advice by the emergency manager. Wind speed and ventilation ratio of building influence highly on the concentration of toxic gas in the building.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".