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Record W4401408905 · doi:10.1016/j.jenvrad.2024.107508

Determination of the source location of anthropogenic radionuclides collected in Finland and Sweden in June 2020 using a multi-technology analysis

2024· article· en· W4401408905 on OpenAlexaff
Ian Hoffman, Pawel Mekarski, Adrian Botti, Yi Jing, Alain Malo, C. Cochrane, Vladimir Khotylev, J. Kastlander, A. Axelsson, Anders Ringbom, Mikael Moring, Tero Karhunen, Aleksi Mattila, Matthew A. Goodwin, Antony N. Davies, Kurt Ungar

Bibliographic record

VenueJournal of Environmental Radioactivity · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsCanadian Nuclear Safety CommissionEnvironment and Climate Change CanadaHealth Canada
Fundersnot available
KeywordsRadionuclideEnvironmental scienceGeographyNuclear physics

Abstract

fetched live from OpenAlex

In June 2020, observations of anthropogenic radionuclides in Estonia, Finland, and Sweden that were not related to any acknowledged environmental release led to a comprehensive investigation on the source and cause of the unusual emissions. Several of the observed radionuclides were on the list of Comprehensive Nuclear-Test-Ban Treaty (CTBT) relevant radionuclides as an indicator of a potential nuclear test, and warranted detailed investigation. While analysis of aerosol samples coupled with Atmospheric Transport and Dispersion Modelling (ATDM) is a standard approach for environmental particulate releases, several new techniques were employed to better characterize the samples that allowed for useful inferences to be made. These inferences were crucial in forming the ultimate hypothesis for determining the facility type and location of the release. • Unattributed anthropogenic radionuclides observed in the Baltic region in 2020. • Coincident spectroscopy and sample characterization help identify source facility. • Combination of tools allows characterizes release to aid with CTBT verification.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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