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Record W4395082757 · doi:10.1080/07370652.2024.2346327

Database of in-plume emission factors from open demilitarization of military ordnance

2024· article· en· W4395082757 on OpenAlexfundaboutno aff
Johanna Aurell, Brian K. Gullett

Bibliographic record

VenueJournal of Energetic Materials · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsnot available
FundersStrategic Environmental Research and Development ProgramAmes Research CenterUniversity of DaytonOffice of Research and DevelopmentU.S. Department of DefenseU.S. Environmental Protection AgencyMinistère de la Défense NationaleNational Aeronautics and Space Administration
KeywordsExplosive materialDetonationSampling (signal processing)Environmental scienceDatabaseUnexploded ordnancePlumeParticulatesPollutantComputer scienceMeteorologyArchaeologyRemote sensingGeologyChemistryGeography

Abstract

fetched live from OpenAlex

A searchable database of emission factors from open burning, open detonation, and static firing of military ordnance and rocket motors has been developed and made available. Sampling and analytical results since 2010 have been compiled from seven campaigns in the USA and Canada at four different sites. Various ordnance types were used for multiple test scenarios varying location, soil covered/uncovered, metal-cased and uncased, and net explosive weight. Target compounds include, variously, particulate matter, metals, energetics, volatile organic compounds, and others. Data were primarily derived from unmanned aerial sampling methods using ordnance charge sizes and procedures representative of operational demilitarization operations. The database includes 1,212 emission factors for 39 different types of munitions and an array of pollutants where open burns accounted for 514 scenarios, static firing for 83, and open detonation for 614. The database will be of use for risk evaluations, environmental reporting requirements, and demilitarization operations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.015
GPT teacher head0.267
Teacher spread0.253 · 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.

Study designBench or experimental
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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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