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Record W4256754599 · doi:10.22215/etd/2021-14402

CANDU Fire Probabilistic Risk Assessment

2021· dissertation· en· W4256754599 on OpenAlexafffund
Hossam Shalabi

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsCarleton University
FundersDivision of Electrical, Communications and Cyber SystemsNational Eye InstituteInternational Atomic Energy AgencyIndiana Family and Social Services AdministrationU.S. Department of EnergyElectric Power Research InstituteCanadian Nuclear Safety Commission
KeywordsShutdownEngineeringNuclear power plantNuclear powerProbabilistic risk assessmentFire safetyEnvironmental scienceForensic engineeringRisk assessmentNuclear engineeringCivil engineeringComputer scienceNuclear physicsComputer security

Abstract

fetched live from OpenAlex

Advancement of fire risk analysis methods has led to a widespread development of detailed fire probabilistic risk assessments (PRA) at nuclear power plants. The Fire PRA assesses the possibility of a fire at critical plant locations and evaluates the fire damage. Fire PRA also evaluates the effect of the fire on safety-related cables and equipment. The scope of the Fire PRA is limited to demonstrating that the fire safe shutdown objectives and performance criteria are met. Hence, the Fire PRA is only used for plant areas where fires may have a potential impact on systems, structures, and components (SSCs) that are required to perform the fire safe shutdown functions.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.950

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.264
Teacher spread0.258 · 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
Published2021
Admission routes2
Has abstractyes

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