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Record W2948344811 · doi:10.1063/1.5088500

A commissioning rod assembly for the Jamaican SLOWPOKE-2 nuclear reactor

2019· article· en· W2948344811 on OpenAlexaffabout
C. Koclas, A. Müftüoğlu, A. Teyssedou, Cornelia Chilian

Bibliographic record

VenueReview of Scientific Instruments · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsUniversité de MontréalPolytechnique Montréal
Fundersnot available
KeywordsProject commissioningNuclear engineeringEnriched uraniumUraniumNuclear reactorResearch reactorNuclear reactor coreOak Ridge National LaboratoryEnvironmental scienceWaste managementEngineeringMaterials sciencePhysicsNuclear physicsNeutronMetallurgyPublishing

Abstract

fetched live from OpenAlex

Within the framework of the Material Management and Minimization Conversion Program of the U.S. Department of Energy's National Nuclear Security Administration, the Argonne National Laboratory approved the manufacturing of a Jamaica's SLOWPOKE-2 nuclear reactor (JM-1) mock-up, reactor removal tools, and commissioning rods by Polytechnique Montreal. This mock-up reactor was then used to practice dry runs of the JM-1 high enriched uranium core removal activities and fresh low enriched uranium core loading operations of the JM-1 reactor conversion. One of the most crucial elements in the commissioning of a new reactor core is the commissioning rod assembly. Hence, this paper presents the design, the fabrication, the calibration, and the dry runs performed at Polytechnique Montreal with commissioning rod assembly. The work was then resumed at Jamaica's International Centre for Environmental and Nuclear Sciences.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.277
Teacher spread0.256 · 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
Published2019
Admission routes2
Has abstractyes

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