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A flight situation advisory system for uninterrupted and efficient air transportation

2024· article· en· W4405072839 on OpenAlexaff
Dmytro Kucherov, Serge Dolgikh, Ihnat Myroshnychenko, Olexii Poshyvailo, Oleksandr Kravchenko

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2024
Typearticle
Languageen
FieldMedicine
TopicTechnology and Human Factors in Education and Health
Canadian institutionsSolana Networks (Canada)
Fundersnot available
KeywordsCrewAviationAir traffic controlRisk analysis (engineering)Reliability (semiconductor)Transport engineeringAir traffic managementOperations researchComputer scienceAeronauticsEngineeringSystems engineeringComputer securityBusiness

Abstract

fetched live from OpenAlex

Abstract Safe and reliable air transportation plays a significant role in modern society by facilitating connection, communication, and economic activity, which has been growing steadily for recent decades. Ensuring a safe, reliable, and efficient operation of the air transportation network is a central challenge in assuring smooth progress in modern and future aviation. This research addresses the problem of human-caused issues in aerial transportation by proposing, substantiating, validating, and designing automated flight situation advisory systems that can provide critical input to the crew in the cases of unexpected and challenging flight situations. It was shown that a proper design and use of these systems could be instrumental in managing flight situations and could eliminate or mitigate a significant fraction of human-cause faults and emergencies. It would expected that such an advisory system can become a proactive and cost-effective tool to improve safety and reliability in the operation of air traffic networks of today and tomorrow. The study’s main result is to assess the importance of factors influencing the development of recommendations by the advisory system.

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)0.000
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 score0.841
Threshold uncertainty score0.256

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 studies0.0000.001
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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes1
Has abstractyes

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