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Record W2974094422 · doi:10.2172/1561637

Molten Salt Reactor Initiating Event and Licensing Basis Event Workshop Summary

2019· report· en· W2974094422 on OpenAlexaboutno aff
David Holcomb, Alex Huning, Askin Guler Yigitoglu, Michael Muhlheim, Willis Poore, George Flanagan

Bibliographic record

Venuenot available
Typereport
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsOak Ridge National LaboratoryEvent (particle physics)Process (computing)Accident (philosophy)EngineeringEnvironmental scienceOperations researchNuclear engineeringComputer scienceNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Oak Ridge National Laboratory (ORNL) hosted a workshop on identifying potential initiating events for radioactive releases as precursors to licensing basis events for a generic liquid-fuel molten salt reactor (MSR). The workshop was held on May 21 and 22, 2019. Participants included representatives from seven prospective reactor vendors, industry bodies, US and Canadian regulators, US and Canadian national laboratories, and the academic community. Accident sequence evaluation is central to deterministic and risk-informed performance-based reactor safety evaluation processes, and initiating events begin the accident sequence evaluation process. The workshop focused on how MSR initiating events feed into the risk-informed, performance-based reactor safety evaluation process described in DG-1353. This report describes the workshop activities and results, provides a generic list of initiating events involving selected systems, and presents the estimation of the relative frequency and consequences of accident sequences for a few important, high-level initiating events.

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)0.010
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.732
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.156
GPT teacher head0.421
Teacher spread0.265 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2019
Admission routes1
Has abstractyes

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