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Record W2901217760 · doi:10.12943/cnr.2017.00016

VERIFYING NUCLEAR WASTE TILE-HOLES USING GAMMA RADIATION SCANNING

2018· article· en· W2901217760 on OpenAlexaffvenue
James W. Johnston, Shuwei Yue, J.E. Stewart

Bibliographic record

VenueCNL Nuclear Review · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsTileRadioactive wasteEnvironmental scienceHigh-level wasteWaste managementRadiationRadioactive sourceRadiation monitoringSpent nuclear fuelNuclear engineeringMaterials scienceNuclear medicineEngineeringPhysicsElectrical engineeringNuclear physicsDetector

Abstract

fetched live from OpenAlex

Nuclear waste management facilities at Chalk River Laboratories (CRL) use below-ground “tile-holes” to store solid waste from various activities such as medical isotope production. After long periods of isotopic decay, some of the waste has decayed down to low activities and can be transferred to low-level waste storage facilities. This paper presents a method to verify the radiation level of the waste inside tile-holes by performing gamma radiation scans along the depth of waste storage tile-holes. Such measurements allow for noninvasive verification of tile-hole contents and provide input to the assessment of radiological risk associated with removal of the waste. Using the radiation profile system, the radiation level of the radioactive waste may be identified based on the radiation profile. This information will support planning for possible transfer of this waste to a licensed waste storage facility designed for low-level waste, thus freeing storage space for possible tile-hole re-use for more highly radioactive waste. CRL-developed small diode-based gamma radiation sensors have been used in these radiation scans. The diode sensors were deployed into verification tubes adjacent to the tile-holes to measure the radiation profile. Over 10 tile-holes have been scanned using this technique since 2009.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score1.000

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)0.0030.001

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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes2
Has abstractyes

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