Machine Learning Insights into Hypersonics Research Evolution: A 21st Century Perspective
Bibliographic record
Abstract
In recent years, the field of hypersonics has witnessed substantial growth in research and development activities, driven by its diverse range of applications spanning both military and commercial sectors. Governments and private companies in several countries have made substantial investments in hypersonic technologies to gain a competitive edge, secure or enhance strategic capabilities, and bolster deterrence measures. In this rapidly evolving landscape, the ability to swiftly and accurately identify emerging technologies becomes paramount. Leveraging the advancements in information technology and computer science, which enable the analysis of vast datasets and the extraction of concealed trends and patterns, this study aims to provide valuable insights to decision-makers in the hypersonics domain. Our focus is on scientific publications related to hypersonics, encompassing the years 2000 to 2020. We employ state-of-the-art natural language processing and machine learning techniques to comprehensively characterize the research landscape. The urgency of this endeavor lies in the necessity for organizations to remain at the forefront of hypersonic research. By algorithmically identifying and tracking 12 key latent research themes and examining their temporal evolution, we offer a structured and objective analysis of the field. Our methodology eliminates subjectivity from the assessment, facilitating consistent comparisons both across topics and across different time intervals. In addition, through our extensive publication similarity analysis, we uncover nuanced patterns that shed light on the cyclical nature of research trends over the two decades under investigation. This comprehensive examination of the hypersonics research landscape not only underscores its critical significance but also provides a robust foundation for informed decision-making. As such, our study serves as a valuable resource for stakeholders seeking to navigate the dynamics of the rapidly advancing field of hypersonics effectively. Received: 2 August 2023 | Revised: 25 September 2023 | Accepted: 12 October 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are retrievable by running the search query mentioned in the manuscript on the data source, i.e., Scopus. In addition, the raw data can be made available upon request. Please contact the corresponding author.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.031 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".