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Record W3019721256 · doi:10.1016/j.jenvrad.2020.106269

Cavity-melt partitioning of refractory radionuclides and implications for detecting underground nuclear explosions

2020· article· en· W3019721256 on OpenAlexfundno aff
Charles R. Carrigan, Yunwei Sun, Éric Pili, Daniel R. Neuville, T. Antoun

Bibliographic record

VenueJournal of Environmental Radioactivity · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
FundersLawrence Livermore National LaboratoryOffice of Defense Nuclear NonproliferationConseil Régional, Île-de-FranceHealth CanadaUniversité de ParisCommissariat à l'Énergie Atomique et aux Énergies AlternativesU.S. Department of Energy
KeywordsDetonationRadionuclideIsotopeNuclear explosionFission productsEnvironmental scienceGeologyNuclear engineeringNuclear physicsChemistryPhysicsExplosive material

Abstract

fetched live from OpenAlex

Isotopic ratios of radioxenon captured in the atmosphere can be indicators of the occurrence of an underground nuclear explosion. However, civilian sources of xenon isotopes, such as medical isotope production facilities and nuclear reactors, can interfere with detection of signals associated with nuclear testing, according to a standard model of the evolution of radioxenon isotopic abundances in a nuclear explosion cavity. We find that this standard model is idealized by not including the effects of physical processes resulting in the partitioning of the radionuclide inventory between a gas phase and rock melt created by the detonation and by ignoring seepage or continuous leakage of gases from the cavity or zone of collapse. Application of more realistic assumptions about the state of the detonation cavity results in isotopic activity ratios that differ from the civilian background more than the idealized standard model suggests, while also reducing the quantity of radioxenon available for atmospheric release and subsequent detection. Our simulations indicate that the physical evolution of the detonation cavity during the post-detonation partitioning process strongly influences isotopic evolution in the gas phase. Collapse of the cavity potentially has the greatest effect on partitioning of the refractory fission products that are precursors to radioxenon. The model allows for the possibility that post-detonation seismicity can be used to predict isotopic evolution.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.519

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 communication0.0000.001
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 head0.025
GPT teacher head0.242
Teacher spread0.216 · 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 designObservational
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

Citations13
Published2020
Admission routes1
Has abstractyes

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