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Record W2104860700 · doi:10.1109/tns.2006.876319

Performance Support System for$hbox I^125$Monitoring in the McMaster University Nuclear Reactor

2006· article· en· W2104860700 on OpenAlexafffund
R. Simionescu, W.F.S. Poehlman, W.J. Garland

Bibliographic record

VenueIEEE Transactions on Nuclear Science · 2006
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsSystems engineeringInterface (matter)SoftwareHuman–machine systemComputer scienceEngineeringEmbedded systemOperating systemArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this paper is to present a design methodology that can be employed to develop computerized radiation monitoring systems for technology insertion into non-computerized human-machine systems. The methodology seeks to hybridize and exploit the benefits of several diverse disciplines including nuclear engineering, computer science, health physics, real-time system design, human-machine interaction, computer-human interface, and software engineering, in the realization of a Performance Support System (PSS). The focus for combining these disciplines is on the implementation of a specific application: The Iodine Monitoring and Advising System (IMAS), a computerized embedded system for the MNR (McMaster Nuclear Reactor) I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">125</sup> production facility. IMAS uses radiation monitoring techniques and human factor concepts to provide nuclear reactor operators with a computerized monitoring alarm facility. The migration from a crude and inflexible spill detector to an intelligent performance support system is documented

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)0.003
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.043
GPT teacher head0.281
Teacher spread0.237 · 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 designSimulation or modeling
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
Published2006
Admission routes2
Has abstractyes

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