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Record W2263222251 · doi:10.2788/718077

National Reachback Systems for Nuclear Security: State-of-play report: ERNCIP Thematic Group Radiological and Nuclear Threats to Critical Infrastructure: Deliverable of task 3.1b

2015· article· en· W2263222251 on OpenAlexaboutno aff
H. Toivonen, Schoech Hubert, P Reppenhagen Grim, Pibida Leticia, James Mark, Weihua Zhang, K. Peräjärvi

Bibliographic record

VenueJoint Research Centre (European Commission) · 2015
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
Fundersnot available
KeywordsDeliverableRadiological weaponTask (project management)State (computer science)Thematic mapTask groupComputer securityBusinessComputer scienceEngineeringMedicineGeographySystems engineeringEngineering managementCartographyRadiology

Abstract

fetched live from OpenAlex

Operational systems for nuclear security in Finland, France, Denmark, UK, US and Canada were reviewed. The Finnish case is a holistic approach to Nuclear Security Detection Architecture, as defined by the International Atomic Energy Agency; reachback is only one\ncomponent of the system, albeit an important crosscutting element of the detection architecture. The French and US studies concentrate on the reachback itself. The Danish nuclear security system is information-driven, relying on the cooperation of the competent authorities. The British and Canadian analyses describe nuclear security planning and operations in a Major Public Event (MPE), Olympics, where cooperation between the frontline officers and the reachback centre plays a key role to reduce radiological and nuclear risks.\nFor the implementation of an efficient reachback system there is a strong need for standardizing the data acquisition, storing, and the final distribution of the analysis results. Major nuclear powers take this activity very seriously and they have 24/7 all year national service for information processing. The case studies of Finland and France show that efficient European reachback is manageable and technically possible on a country-wide basis. The case study on Denmark reveals that countries with limited reachback resources need an adequate and standardized technical information sharing mechanism to aid their national analysis services in a precise and timely manner.

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)0.002
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 head0.068
GPT teacher head0.316
Teacher spread0.247 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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