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Record W4412845971 · doi:10.18280/acsm.490306

Comparative Review of Gamma Ray Shielding Properties of Building and Metallic Materials Using Experimental and Theoretical Methods

2025· article· en· W4412845971 on OpenAlexvenueno aff
M.E.M. Eisa

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsnot available
FundersNorthern Border University
KeywordsElectromagnetic shieldingMaterials scienceMetalComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The assessment in this review paper is founded on the synthesis of experimental and theoretical investigations, which methodically appraise the gamma-ray shielding capabilities of prevalent materials.These materials encompass lead (Pb), iron (Fe), concrete, cement, and clay.The evaluation of shielding performance involved the use of gamma sources Cs-137 and Co-60, along with critical metrics such as the half-value layer (HVL) and the linear attenuation coefficient (LAC).The findings demonstrate that lead exhibits superior attenuation properties compared to iron, while clay, cement, and concrete demonstrate significantly inferior attenuating capabilities.The use of composite shielding combinations, such as Pb + Fe and Pb + cement, has been demonstrated to enhance attenuation efficiency.The review underscores two notable aspects.Firstly, it highlights the pressing need for substance optimization in radiation protection applications, and secondly, it outlines the prospective benefits of composite shielding.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.084
GPT teacher head0.398
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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