Bibliographic record
Abstract
Overview Cost analysis and manufacturing technology are subjects that require specialized instruction in academies, and they are not the main topics of this book. They are included to make readers aware that the classical aeronautical subjects of aerodynamics, structures, and propulsion are not sufficient for a successful aircraft design. Cost analysis and manufacturing technology must be considered during the conceptual design study and integrated with classical aeronautical subjects. The following terms are used extensively in this chapter; some were referred to previously: Design Built Team (DBT) : This is a team of hand-picked, experienced engineers and specialists drawn from various related disciplines, who synthesize design for DFM/A considerations in multidisciplinary interactions with the classical subjects. Design for Manufacture and Assembly (DFM/A) : This is an engineering approach with the object of minimizing costs of production without sacrificing design integrity. Integrated Product and Process Development (IPPD) (also known as Concurrent Engineering) : This offers an environment in which DBT uses IPPD to synthesize the trade-off studies in a multidisciplinary study to arrive at the best value for the product as a global optimum, rather than optimizing to a particular design study. DFM/A is part of IPPD. Design for Six Sigma (DFSS) : This is an integrated approach to design with the key issue of reducing the scope of mistakes and inefficiencies – that is, making a product right the first time to prevent the waste of company resources. […]
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.026 |
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".