Bibliographic record
Abstract
Overview Chapter 6 illustrates how to arrive at a preliminary aircraft configuration of a new project starting from scratch, with the expectancy of satisfying the market specification. To progress further, the next task is to lay out the undercarriage (also known as the landing gear) position relative to the aircraft CG, which is accurately established in Chapter 8. This chapter addresses the undercarriage quite extensively but not the detailed design; rather, it focuses on those aspects related to undercarriage layout and sizing during the conceptual study phase. More details on undercarriage design are in the cited references. This chapter first introduces the undercarriage to serve vehicle ground handling, followed by basic definitions, terminologies, and information used in the design process and integration with an aircraft. Finally, methodologies for layout of the undercarriage and tire sizing are presented to complete the aircraft configuration generated thus far. Considerable attention is required to lay out the undercarriage position and to determine tire size and geometric details to avoid hazards during operation. This book limits the topic to the fundamentals to the extent of the requirements for positioning the undercarriage and sizing the wheels and tires. These fundamentals are shown schematically in the three-view aircraft drawings. Relevant information on wheel tires is also presented in this chapter. The undercarriage is a complex and heavy item and, therefore, expensive to manufacture. It should be made right the first time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".