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Record W2296640724 · doi:10.2172/1212248

Particle Release Experiment (PRex) (Final Report)

2014· report· en· W2296640724 on OpenAlexaboutno aff
Martin E. Keillor, Leah M. Arrigo, R. Detwiler, Warnick J. Kernan, R.R. Kirkham, Matthew R. MacDougall, Veraun Chipman, B. D. Milbrath, Jeremy P. Rishel, A. Seifert, Carolyn E. Seifert, John E. Smart, D.F. Emer

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
FundersPacific Northwest National LaboratoryNational Nuclear Security Administration
KeywordsEnvironmental sciencePlumeContext (archaeology)RadionuclideSampling (signal processing)Waste managementNuclear physicsMeteorologyEngineeringArchaeologyPhysicsFilter (signal processing)Geography

Abstract

fetched live from OpenAlex

An experiment to release radioactive particles representative of small-scale venting from an underground nuclear test was conducted to gather data in support of treaty verification and monitoring activities. For this experiment, a CO<sub>2</sub>-driven “air cannon” was used to release La-140 at ambient temperatures. Lanthanum-140 was chosen to represent the fission fragments because of its short half-life and prominent gamma-ray emissions; the choice was also influenced by the successful production and use of La-140 with low levels of radioactive contaminants in a Defence Research and Development Canada Field Trial. The source was created through activation of high-purity natural lanthanum oxide at the reactor of Washington State University, Pullman, Washington. Multiple varieties of witness plates and air samplers were laid in an irregular grid covering the area over which the plume was modeled to deposit. Aerial survey, a NaI(Tl) mobile spectrometer, and handheld and backpack instruments ranging from polyvinyl toluene to high-purity germanium were used to survey the plume. Additionally, three varieties of soil sampling were investigated. The relative sensitivity and utility of sampling and survey methods are discussed in the context of On-Site Inspection. The measurements and samples show a high degree of correlation and form a valuable set of test data.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.758
Threshold uncertainty score0.999

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)0.0320.002

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 head0.040
GPT teacher head0.293
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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2014
Admission routes1
Has abstractyes

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