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Record W2754251367 · doi:10.12943/cnr.2017.00008

EMERGING AREAS OF SHIELDING RESEARCH—A BIRD’S EYE VIEW

2017· article· en· W2754251367 on OpenAlexaffvenueabout
Ruxandra Dranga, Frederick P. Adams

Bibliographic record

VenueCNL Nuclear Review · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsNuclear decommissioningElectromagnetic shieldingSystems engineeringNuclear powerEngineering managementWork (physics)Computer scienceNuclear scienceRisk analysis (engineering)EngineeringBusinessMechanical engineeringElectrical engineeringNuclear engineeringPhysics

Abstract

fetched live from OpenAlex

Shielding analysis and design are important tools for ensuring that humans and the environment are protected from the detrimental effects of high levels of radiation. The fundamental principles and methodologies for shielding analysis and design, especially for reactor applications, have been developed and refined since the 1940s and the beginning of nuclear power research programs in Canada and internationally. Other applications are gaining importance due to both increased need and technological advances. In this work, a high-level survey of emerging areas in shielding research and development is provided. Areas of topical interest include remote reactor monitoring, source reconstruction and inverse shielding methods, waste management and decommissioning applications, accelerator, cyclotron, and other advanced medical shielding applications, space exploration, and new materials development. Each of these areas of interest is evaluated based on current capacity of the research community. They are also evaluated in terms of the benefits for the scientific community and industry arising from performing research including development of new technologies and techniques.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.066
GPT teacher head0.404
Teacher spread0.339 · 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
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
Published2017
Admission routes3
Has abstractyes

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