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Record W4317637363 · doi:10.2514/6.2023-2397

Maverick and Skunk Works: Representing Aerospace in Popular Culture

2023· article· en· W4317637363 on OpenAlex
James Walton, Patrick LeBeau

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

VenueAIAA SCITECH 2023 Forum · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsStudioAerospaceAeronauticsWork (physics)EngineeringCharacter (mathematics)Test (biology)Production (economics)Architectural engineeringComputer scienceAerospace engineeringTelecommunicationsMechanical engineering

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2023-2397.vid Lockheed Martin Skunk Works was approached in the summer of 2017 by Paramount Studios to provide technical support for the long-awaited second chapter of the movie, Top Gun. In the second installment, Top Gun: Maverick, the story called for a manned hypersonic test aircraft that the lead character, Maverick, would fly and push to its limits, as only Maverick can. This paper describes how the relationship between the Lockheed Martin Skunk Works and the Paramount Studios Production Team started, the development of the Darkstar concept, the building of a full-scale mockup for filming, and continued support of the production during script development and filming. Further, and arguably the most important part of this paper, we will discuss the impact of doing a very out-of-the ordinary project (for us) on the Skunk Works team directly responsible for the work, and how very public projects like this can help the Company, and also the industry at large.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.261
Teacher spread0.250 · 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