Recovery Method for Emergency Situations with Hazardous Substances Emission into the Atmosphere
Bibliographic record
Abstract
The objective of this Article is development of a hazardous area extent reducing method in the atmosphere upon emergency situation occurrence with hazardous chemical and radioactive substances emission. To achieve the specified objective it is recommended to use deposition of gaseous, liquid and solid disperse hazardous particles from the atmosphere using artificial precipitation over a zone of emergency. For artificial intensification of precipitation, use of chemical and physical principles of impact on droplets formation processes in the area of clouds is proposed. A pyrotechnic composition was developed for chemical impact, which when combustion generates finely dispersed chemical centers of condensation and modifying components for hazardous chemical substances neutralization. Conducting the ionization of precipitation formation area by a strong impulse electromagnetic irradiation was proposed as a physical impact on the artificial precipitation formation processes. The laboratory study results confirmed the technical feasibility of this method implementation. To reduce the necessary radiator power, application of a multi-position radiators system with radiation focusing to one irradiation zone was proposed. The theoretical and experimental results obtained in the article are the base for development of emergency situation negative consequences reduction practical procedures through artificial precipitation initiation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".