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Record W4403287662 · doi:10.1016/j.jclepro.2024.143910

Simplified efficiency calibration methods for scintillation detectors used in nuclear remediation

2024· article· en· W4403287662 on OpenAlexaff
V. V. Golovko

Bibliographic record

VenueJournal of Cleaner Production · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsCalibrationEnvironmental remediationEnvironmental scienceScintillationDetectorNuclear engineeringLiquid scintillation countingProcess engineeringWaste managementNuclear physicsRadiochemistryPhysicsEngineeringChemistryContaminationOptics

Abstract

fetched live from OpenAlex

Our study introduces innovative methods to address waste reduction and enhance resource efficiency in nuclear cleanup processes. We focus on sustainable nuclear technology, aligning with goals for cleaner production and environmental protection. We developed two novel methods, termed “oversimplified” and “simplified,” for easily determining the photopeak efficiency of NaI(Tl) scintillation detectors. These methods, along with a “general” solution method, were validated with calibrated radioactive sources such as 241 Am, 57 Co, 133 Ba, 137 Cs, and 60 Co, and were used to commission a NaI(Tl) scintillation detector system at Chalk River Laboratories (CRL) for nuclear remediation. The system demonstrated high accuracy, making it suitable for screening unstable isotopes at contaminated sites. This screening tool significantly reduces the number of soil samples requiring detailed characterization, thereby lowering operational costs. The NaI(Tl) detector system was calibrated for near-contact geometry, which is commonly used at CRLs. Detection limits were established for this configuration. By improving the efficiency calibration of scintillation detectors, our study advances sustainable nuclear remediation practices, promoting environmental sustainability and cleaner production processes. • The developed “oversimplified” and “simplified” calibration methods enhance the efficiency determination of NaI(Tl) scintillation detectors, contributing to more sustainable nuclear technology and cleaner production practices. These methods are validated with various radioactive sources and align with environmental sustainability goals. • The study demonstrated that the calibrated NaI(Tl) detector system meets all acceptance criteria for near-contact geometry, ensuring reliable screening of radioactive isotopes at contaminated sites. This reduces the amount of radioactive waste requiring detailed characterization, thereby lowering operational costs for nuclear remediation. • The research effectively employs R, a noncommercial and open-source data analysis tool, to analyze data from scintillation detectors. This approach not only reduces software licensing costs but also leverages the extensive verification processes of the broader scientific community, enhancing the reliability and accuracy of nuclear remediation activities.

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)0.005
metaresearch head score (Gemma)0.010
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.335
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 designBench or experimental
Domainnot available
GenreMethods

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

Citations7
Published2024
Admission routes1
Has abstractyes

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