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Record W3085968172 · doi:10.1109/tasc.2020.3022993

General Failure Modes and Effects Analysis for Accelerator and Detector Magnet Design at JLab

2020· article· en· W3085968172 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.

Bibliographic record

VenueIEEE Transactions on Applied Superconductivity · 2020
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceSuperconducting magnetIdentification (biology)Process (computing)MagnetDetectorReliability engineeringRisk analysis (engineering)Key (lock)Risk managementNuclear engineeringSystems engineeringMechanical engineeringEngineeringComputer securityBusinessTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

The aim of this article is to develop a risk management procedure, which could be applied to the magnet design process, for both superconducting and normal magnets at the Jefferson Laboratory (JLab). This procedure allowed us to identify the key risks at each of the critical phases of design and propose procedures, tests, and checks to mitigate each risk. In this article, we present a qualitative and quantitative risk management procedure commonly referred to a “failure modes and effects analysis.” As part of this procedure, we calculated a risk priority number (RPN) for each activity of the process, identified the most critical activities and proposed mitigation activities, which in turn resulted in a revised RPN. Another benefit of this procedure was the identification of appropriate “control and hold” points within the design process, which allowed one to review and approve a particular outcome before proceeding to the next sequential activity.

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 score0.373
Threshold uncertainty score1.000

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 head0.032
GPT teacher head0.218
Teacher spread0.185 · 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