Review of augmented reality in aerospace industry
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.
Bibliographic record
Abstract
Purpose The purpose of this paper is to assess and determine the potential of augmented reality (AR) in aerospace applications through a survey of published sources. Design/methodology/approach This paper reviews a database of AR applications developed for the aerospace sector in academic research or industrial training and operations. The review process begins with the classification of these applications, followed by a brief discussion on the implications of AR technology in each category. Findings AR is abundantly applied in engineering, navigation, training and simulation. There is potential for application in in-flight entertainment and communication, crew support and airport operations monitoring. Originality/value This paper is a general review introducing existing and potential AR applications in various fields of the aerospace industry. Unlike previous publications, this article summarizes existing and emerging applications to familiarize readers with AR use in all of aerospace. The paper outlines example projects and creates a single comprehensive reference of AR advancements and its use in the aerospace industry. The paper provides individuals with a quick guide to available and emerging technology.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it