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Record W2503454666 · doi:10.5539/apr.v8n4p92

X-ray Attenuation and Reduction of Backscattered Radiation

2016· article· en· W2503454666 on OpenAlexvenueno aff
Abdullah Taher Naji, Mohamad Suhaimi Jaafar, Esmail Abdo Mohammed Ali, S. K. J. Al‐Ani

Bibliographic record

VenueApplied Physics Research · 2016
Typearticle
Languageen
FieldMaterials Science
TopicRadiation Shielding Materials Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAttenuationShieldsMaterials scienceShieldBackscatter (email)AluminiumX-rayRadiationOpticsElectromagnetic shieldingComposite materialComputer sciencePhysicsTelecommunications

Abstract

fetched live from OpenAlex

This paper reports the comparative study of X-ray attenuation using different materials which can be utilized to design anti-backscattered grids and radiation protection shields. The characteristics of X-ray attenuation in several materials, namely, lead, copper, iron steel, and aluminum, are investigated under diverse exposure parameters to measure their ability to attenuate incident X-ray and their capability to reduce backscattered X-ray. Lead and iron steel shields exhibit the best abilities in attenuating X-ray, whereas aluminum shield shows the least attenuation ability. This study proposes a design of a unique method for assessing the backscatter X-ray dose. The iron steel grid based on iron steel plate provides the best value in reducing backscattered radiation (up to 34.78%) compared with other conventional plate materials.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.340
Teacher spread0.284 · 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 designBench or experimental
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

Citations11
Published2016
Admission routes1
Has abstractyes

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