Maverick and Skunk Works: Representing Aerospace in Popular Culture
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.149 | 0.018 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".