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Record W4413459509 · doi:10.3997/2214-4609.202520017

Radiation Portal Monitors for Characterization of Radioactive Waste Containment

2025· article· en· W4413459509 on OpenAlexaboutno aff
Nicolas Martin-Burtart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsnot available
Fundersnot available
KeywordsContainment (computer programming)Radioactive wasteWaste managementEnvironmental scienceNuclear engineeringCharacterization (materials science)Radiation monitoringRadiochemistryComputer scienceMaterials scienceEngineeringChemistryNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Summary Radiation portal monitors are large volume radiation detectors typically positioned along roadways, railways, and pedestrian portals. They are typically used for the detection of radioactive material where no such material is expected. Applications include monitoring vehicles and personnel exiting nuclear facilities, at the entrance to steel and scrap metal facilities, and at ports of entry, such as international borders and ports. The Port Hope Project involves the cleanup of approximately 1.2 million cubic metres of historic low-level radioactive waste from various sites in Port Hope. The waste is a consequence of past practices involving the refining of radium and uranium by a former federal Crown corporation, Eldorado Nuclear Limited, and its private-sector predecessors. It involves the construction of a new Long-Term Waste Management Facility at the site of an existing, closed low-level radioactive waste management facility located in Ontario, Canada. Each truck passes through a radiation portal monitor coupled with a scale when travelling between the waste location and waste storage facility. The radiation portal monitors have been calibrated to provide an estimate of the total radioactivity being deposited into the new waste facility.. This paper describes the methodology developed for such a project.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.042
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

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