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Record W3204683726 · doi:10.18280/ijsse.110415

Recovery Method for Emergency Situations with Hazardous Substances Emission into the Atmosphere

2021· article· en· W3204683726 on OpenAlexvenueno aff
Maksym Kustov, Volodymyr D. Kalugin, Olena V. Hristich, Yuliana Hapon

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Industrial Safety
Canadian institutionsnot available
Fundersnot available
KeywordsHazardous wasteEnvironmental scienceAtmosphere (unit)PrecipitationCombustionHazardous substanceWaste managementMeteorologyChemistryEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.230
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2021
Admission routes1
Has abstractyes

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