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Record W4399844294 · doi:10.1088/1361-6498/ad5a17

Activity estimates of localised gamma emitting radionuclides using a GR-135 survey meter

2024· article· en· W4399844294 on OpenAlexafffund
Brian Bewer

Bibliographic record

VenueJournal of Radiological Protection · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsCanadian Light Source (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchGovernment of SaskatchewanCanada Foundation for InnovationUniversity of Saskatchewan
KeywordsRadionuclideMetreEnvironmental scienceContaminationRadioactive wasteRadiochemistryRadiation monitoringGamma rayRadioactive contaminationNuclear engineeringPhysicsChemistryNuclear physicsEngineering

Abstract

fetched live from OpenAlex

During the operation of high energy accelerators activated materials are commonly created. The activity and isotopes present in these materials must be characterised for their clearance and release from the facility, or to ascertain their duration of stay in a radiological storage area. An activity estimate method using a gamma detecting GR-135 survey meter, which has the ability to collect an energy spectrum, is presented. Using several reference radioactive sources the detection efficiency and dead time of the survey meter were characterised. This information combined with the physical properties of the survey meter, the counting time and the properties of the measured photon energy emissions can be used to calculate an accurate activity estimate for localised activation on accelerator components, or loose contamination on isolated waste 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.092
GPT teacher head0.316
Teacher spread0.224 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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