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Record W4214773694 · doi:10.3390/su14052643

An Exploratory Research on Blockchain in Aviation: The Case of Maintenance, Repair and Overhaul (MRO) Organizations

2022· article· en· W4214773694 on OpenAlex
Marina Efthymiou, Katie A. McCarthy, Chris Markou, John F. O’Connell

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

VenueSustainability · 2022
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsInternational Air Transport Association
Fundersnot available
KeywordsBlockchainIntermediaryBusinessExploratory researchAviationAircraft maintenanceProcess managementEngineering managementOperations managementComputer securityEngineeringComputer scienceMarketingAeronautics

Abstract

fetched live from OpenAlex

The aircraft maintenance sector has high complexity with many intermediaries, multiple actors sharing data and needs to ensure high data security. The implementation of Blockchain technology can significantly contribute to the aforementioned characteristics. This research explores the implementation of Blockchain technology in Maintenance, Repair and Overhaul (MRO). For this research, a mixed-method approach was followed to obtain a holistic view of the adoption of Blockchain technology in an aircraft maintenance facility. Semi-structured interviews and a case study were used. The interview findings related to the current status of maintenance records management. The findings also highlighted the value of data storage within MRO’s and the benefits of Blockchain. The paper also discusses the readiness/willingness of aircraft maintenance facilities to implement Blockchain and the barriers to implementation. Recommendations and areas for further research are identified.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.309
Teacher spread0.293 · 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