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Record W2000035092 · doi:10.1145/2038642.2038676

Software certification experience in the canadian nuclear industry

2011· article· en· W2000035092 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSafety Systems Engineering in Autonomy
Canadian institutionsMcMaster University
FundersU.S. Food and Drug Administration
KeywordsCertificationLicenseeProcess (computing)EngineeringComputer scienceWork (physics)Software engineeringEngineering managementLicenseOperating systemPolitical scienceLaw

Abstract

fetched live from OpenAlex

The computer controlled shutdown systems for the Nuclear Power Generating Station at Darlington, Canada, have been subject to licensing scrutinization on a number of occasions. After the first licence was approved in 1990, the licensee, Ontario Hydro, was given a number of years by the regulator to redesign the shutdown systems so that they would be more maintainable. This paper briefly describes the original certification process, lessons learned, and the subsequent development and certification of the shutdown systems. The development, internal certification processes and the regulator's certification process are briefly described. Although twenty years has elapsed since this work started, and there are new analysis techniques and tools that could be applied today, the original process itself has withstood the test of time extraordinarily well. This paper describes principles that explain why it was so successful, and how we can develop more modern approaches from this experience.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.978

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 head0.056
GPT teacher head0.213
Teacher spread0.157 · 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

Quick stats

Citations11
Published2011
Admission routes2
Has abstractyes

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