Bibliographic record
Abstract
Overview An aircraft must ascend to heights by defying gravity and sustain the tiring task of cruise – naturally, it is weight-sensitive. Anyone who has climbed a hill knows about this experience, especially if one has to carry baggage. An inanimate aircraft is no exception; its performance suffers by carrying unnecessary mass (i.e., weight). At the conceptual design stage, aircraft designers have a daunting task of creating a structure not only at a low weight but also at a low cost, without sacrificing safety. Engineers also must be accurate in weight estimation, well ahead of manufacture. This chapter presents a formal method to predict an aircraft and its component mass (i.e., weight), which results in locating the CG during the conceptual design phase. The aircraft inertia estimation is not within the scope of this book. In the past, aircraft weight was expressed in FPS units in pound (lb) weight in the United Kingdom and the United States. With the use of kg as mass in SI, the unit for weight is a Newton , which is calculated as the mass multiplied by gravitational acceleration (9.81 m/s 2 ). This book uses both the FPS and SI systems; this chapter addresses mass in SI and weight in FPS, sometimes interchangeably. Material strength contributes to structural integrity. As stated previously, aircraft conceptual designers must have broad-based knowledge in all aspects of technology; in this case, they must have a sound knowledge in material properties (e.g., strength-to-weight and strength-to-cost ratios).
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".