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Record W2342477729

Multi-state system in a fault tree analsis of a nuclear based thermochemical hydrogen plant

2008· dissertation· en· W2342477729 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuee-scholar@UOIT (University of Ontario Institute of Technology) · 2008
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Power Generation Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFault tree analysisState (computer science)Computer scienceReliability engineeringEngineeringAlgorithm
DOInot available

Abstract

fetched live from OpenAlex

Nuclear-based hydrogen generation is a promising way to supply hydrogen for this large market in the future. This thesis focuses on one of the most promising methods, a thermochemical Cu-Cl cycle, which is currently under development by UOIT, Atomic Energy of Canada Limited (AECL) and the Argonne National Laboratory (ANL).\nThe safety issues of the Cu-Cl cycle are addressed in this thesis. An investigation of major accident scenarios shows that potential tragedies can be avoided with effective risk analysis and safety management programs. As a powerful and systematic tool, fault tree analysis (FTA) is adapted to the particular needs of the Cu-Cl system. This thesis develops a new method that combines FTA with a reliability analysis tool, multi-state system (MSS), to improve the accuracy of FTA and also improve system reliability.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.189
Teacher spread0.179 · 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